Overview

Dataset statistics

Number of variables85
Number of observations614
Missing cells37101
Missing cells (%)71.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory407.9 KiB
Average record size in memory680.2 B

Variable types

Categorical77
Numeric8

Warnings

Periodo has a high cardinality: 612 distinct values High cardinality
Imacec_empalmado has a high cardinality: 298 distinct values High cardinality
Imacec_produccion_de_bienes has a high cardinality: 298 distinct values High cardinality
Imacec_minero has a high cardinality: 298 distinct values High cardinality
Imacec_industria has a high cardinality: 299 distinct values High cardinality
Imacec_resto_de_bienes has a high cardinality: 298 distinct values High cardinality
Imacec_comercio has a high cardinality: 299 distinct values High cardinality
Imacec_servicios has a high cardinality: 298 distinct values High cardinality
Imacec_a_costo_de_factores has a high cardinality: 298 distinct values High cardinality
Imacec_no_minero has a high cardinality: 298 distinct values High cardinality
PIB_Agropecuario_silvicola has a high cardinality: 94 distinct values High cardinality
PIB_Pesca has a high cardinality: 93 distinct values High cardinality
PIB_Mineria has a high cardinality: 93 distinct values High cardinality
PIB_Mineria_del_cobre has a high cardinality: 93 distinct values High cardinality
PIB_Otras_actividades_mineras has a high cardinality: 94 distinct values High cardinality
PIB_Industria_Manufacturera has a high cardinality: 94 distinct values High cardinality
PIB_Alimentos has a high cardinality: 93 distinct values High cardinality
PIB_Bebidas_y_tabaco has a high cardinality: 93 distinct values High cardinality
PIB_Textil has a high cardinality: 93 distinct values High cardinality
PIB_Maderas_y_muebles has a high cardinality: 93 distinct values High cardinality
PIB_Celulosa has a high cardinality: 93 distinct values High cardinality
PIB_Refinacion_de_petroleo has a high cardinality: 93 distinct values High cardinality
PIB_Quimica has a high cardinality: 94 distinct values High cardinality
PIB_Minerales_no_metalicos_y_metalica_basica has a high cardinality: 94 distinct values High cardinality
PIB_Productos_metalicos has a high cardinality: 93 distinct values High cardinality
PIB_Electricidad has a high cardinality: 93 distinct values High cardinality
PIB_Construccion has a high cardinality: 93 distinct values High cardinality
PIB_Comercio has a high cardinality: 93 distinct values High cardinality
PIB_Restaurantes_y_hoteles has a high cardinality: 93 distinct values High cardinality
PIB_Transporte has a high cardinality: 93 distinct values High cardinality
PIB_Comunicaciones has a high cardinality: 93 distinct values High cardinality
PIB_Servicios_financieros has a high cardinality: 93 distinct values High cardinality
PIB_Servicios_empresariales has a high cardinality: 93 distinct values High cardinality
PIB_Servicios_de_vivienda has a high cardinality: 93 distinct values High cardinality
PIB_Servicios_personales has a high cardinality: 93 distinct values High cardinality
PIB_Administracion_publica has a high cardinality: 93 distinct values High cardinality
PIB_a_costo_de_factores has a high cardinality: 93 distinct values High cardinality
Impuesto_al_valor_agregado has a high cardinality: 93 distinct values High cardinality
Derechos_de_Importacion has a high cardinality: 93 distinct values High cardinality
PIB has a high cardinality: 93 distinct values High cardinality
Precio_de_la_gasolina_en_EEUU_dolaresm3 has a high cardinality: 581 distinct values High cardinality
Precio_del_cobre_refinado_BML_dolareslibra has a high cardinality: 474 distinct values High cardinality
Precio_del_kerosene_dolaresm3 has a high cardinality: 250 distinct values High cardinality
Tipo_de_cambio_del_dolar_observado_diario has a high cardinality: 460 distinct values High cardinality
Ocupados has a high cardinality: 128 distinct values High cardinality
Ocupacion_en_Agricultura_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Explotacion_de_minas_y_canteras_INE has a high cardinality: 93 distinct values High cardinality
Ocupacion_en_Industrias_manufactureras_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Suministro_de_electricidad_INE has a high cardinality: 93 distinct values High cardinality
Ocupacion_en_Actividades_de_servicios_administrativos_y_de_apoyo_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Actividades_profesionales_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Actividades_inmobiliarias_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Actividades_financieras_y_de_seguros_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Informacion_y_comunicaciones_INE has a high cardinality: 93 distinct values High cardinality
Ocupacion_en_Transporte_y_almacenamiento_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Actividades_de_alojamiento_y_de_servicio_de_comidas_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Construccion_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Comercio_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Suministro_de_agua_evacuacion_de_aguas_residuales_INE has a high cardinality: 93 distinct values High cardinality
Ocupacion_en_Administracion_publica_y_defensa_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Enseanza_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Actividades_artisticas_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Otras_actividades_de_servicios_INE has a high cardinality: 93 distinct values High cardinality
Ocupacion_en_Actividades_de_los_hogares_como_empleadores_INE has a high cardinality: 92 distinct values High cardinality
Ocupacion_en_Actividades_de_organizaciones_y_organos_extraterritoriales_INE has a high cardinality: 89 distinct values High cardinality
Tipo_de_cambio_nominal_multilateral___TCM has a high cardinality: 311 distinct values High cardinality
Indice_de_tipo_de_cambio_real___TCR_promedio_1986_100 has a high cardinality: 418 distinct values High cardinality
Indice_de_produccion_industrial has a high cardinality: 142 distinct values High cardinality
Indice_de_produccion_industrial__mineria has a high cardinality: 355 distinct values High cardinality
Indice_de_produccion_industrial_electricidad__gas_y_agua has a high cardinality: 82 distinct values High cardinality
Indice_de_produccion_industrial__manufacturera has a high cardinality: 340 distinct values High cardinality
Generacion_de_energia_electrica_CDEC_GWh has a high cardinality: 262 distinct values High cardinality
Indice_de_ventas_comercio_real_IVCM has a high cardinality: 82 distinct values High cardinality
Indice_de_ventas_comercio_real_no_durables_IVCM has a high cardinality: 82 distinct values High cardinality
Indice_de_ventas_comercio_real_durables_IVCM has a high cardinality: 83 distinct values High cardinality
Precio_de_la_onza_troy_de_oro_dolaresoz is highly correlated with Precio_de_la_onza_troy_de_plata_dolaresoz and 2 other fieldsHigh correlation
Precio_de_la_onza_troy_de_plata_dolaresoz is highly correlated with Precio_de_la_onza_troy_de_oro_dolaresoz and 2 other fieldsHigh correlation
Precio_del_diesel_centavos_de_dolargalon is highly correlated with Precio_del_petroleo_Brent_dolaresbarril and 2 other fieldsHigh correlation
Precio_del_gas_natural_dolaresmillon_de_unidades_termicas_britanicas is highly correlated with Precio_del_propano_centavos_de_dolargalon_DTNHigh correlation
Precio_del_petroleo_Brent_dolaresbarril is highly correlated with Precio_de_la_onza_troy_de_oro_dolaresoz and 4 other fieldsHigh correlation
Precio_del_petroleo_WTI_dolaresbarril is highly correlated with Precio_de_la_onza_troy_de_oro_dolaresoz and 4 other fieldsHigh correlation
Precio_del_propano_centavos_de_dolargalon_DTN is highly correlated with Precio_del_diesel_centavos_de_dolargalon and 3 other fieldsHigh correlation
Precio_de_la_onza_troy_de_oro_dolaresoz is highly correlated with Precio_de_la_onza_troy_de_plata_dolaresoz and 3 other fieldsHigh correlation
Precio_de_la_onza_troy_de_plata_dolaresoz is highly correlated with Precio_de_la_onza_troy_de_oro_dolaresoz and 2 other fieldsHigh correlation
Precio_del_diesel_centavos_de_dolargalon is highly correlated with Precio_del_petroleo_Brent_dolaresbarril and 2 other fieldsHigh correlation
Precio_del_gas_natural_dolaresmillon_de_unidades_termicas_britanicas is highly correlated with Precio_de_la_onza_troy_de_oro_dolaresoz and 1 other fieldsHigh correlation
Precio_del_petroleo_Brent_dolaresbarril is highly correlated with Precio_de_la_onza_troy_de_oro_dolaresoz and 4 other fieldsHigh correlation
Precio_del_petroleo_WTI_dolaresbarril is highly correlated with Precio_de_la_onza_troy_de_oro_dolaresoz and 4 other fieldsHigh correlation
Precio_del_propano_centavos_de_dolargalon_DTN is highly correlated with Precio_del_diesel_centavos_de_dolargalon and 3 other fieldsHigh correlation
Precio_de_la_onza_troy_de_oro_dolaresoz is highly correlated with Precio_de_la_onza_troy_de_plata_dolaresozHigh correlation
Precio_de_la_onza_troy_de_plata_dolaresoz is highly correlated with Precio_de_la_onza_troy_de_oro_dolaresoz and 2 other fieldsHigh correlation
Precio_del_diesel_centavos_de_dolargalon is highly correlated with Precio_del_petroleo_Brent_dolaresbarril and 2 other fieldsHigh correlation
Precio_del_gas_natural_dolaresmillon_de_unidades_termicas_britanicas is highly correlated with Precio_del_propano_centavos_de_dolargalon_DTNHigh correlation
Precio_del_petroleo_Brent_dolaresbarril is highly correlated with Precio_de_la_onza_troy_de_plata_dolaresoz and 3 other fieldsHigh correlation
Precio_del_petroleo_WTI_dolaresbarril is highly correlated with Precio_de_la_onza_troy_de_plata_dolaresoz and 3 other fieldsHigh correlation
Precio_del_propano_centavos_de_dolargalon_DTN is highly correlated with Precio_del_diesel_centavos_de_dolargalon and 3 other fieldsHigh correlation
Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE is highly correlated with Precio_del_diesel_centavos_de_dolargalon and 61 other fieldsHigh correlation
Precio_del_diesel_centavos_de_dolargalon is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 60 other fieldsHigh correlation
PIB_Productos_metalicos is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Actividades_de_alojamiento_y_de_servicio_de_comidas_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Servicios_empresariales is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Construccion is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Refinacion_de_petroleo is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Celulosa is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Industrias_manufactureras_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Actividades_financieras_y_de_seguros_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Transporte_y_almacenamiento_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Comercio_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Servicios_financieros is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Otras_actividades_de_servicios_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Actividades_de_los_hogares_como_empleadores_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Explotacion_de_minas_y_canteras_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Administracion_publica is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Textil is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Otras_actividades_mineras is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Transporte is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Minerales_no_metalicos_y_metalica_basica is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Informacion_y_comunicaciones_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Administracion_publica_y_defensa_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Indice_de_produccion_industrial_electricidad__gas_y_agua is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Enseanza_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Comercio is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_a_costo_de_factores is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Mineria is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Agricultura_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Servicios_personales is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Construccion_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Actividades_artisticas_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Actividades_profesionales_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Suministro_de_electricidad_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Indice_de_ventas_comercio_real_durables_IVCM is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Suministro_de_agua_evacuacion_de_aguas_residuales_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 59 other fieldsHigh correlation
Indice_de_ventas_comercio_real_no_durables_IVCM is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Indice_de_ventas_comercio_real_IVCM is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Mineria_del_cobre is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Maderas_y_muebles is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Precio_de_la_onza_troy_de_oro_dolaresoz is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 59 other fieldsHigh correlation
PIB_Quimica is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Industria_Manufacturera is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Agropecuario_silvicola is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Precio_del_petroleo_Brent_dolaresbarril is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 60 other fieldsHigh correlation
PIB_Restaurantes_y_hoteles is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ventas_autos_nuevos is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 57 other fieldsHigh correlation
PIB_Pesca is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Precio_del_propano_centavos_de_dolargalon_DTN is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 60 other fieldsHigh correlation
Ocupacion_en_Actividades_de_organizaciones_y_organos_extraterritoriales_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 58 other fieldsHigh correlation
PIB_Servicios_de_vivienda is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Alimentos is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
No_sabe__No_responde_Miles_de_personas is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Precio_del_petroleo_WTI_dolaresbarril is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 60 other fieldsHigh correlation
Derechos_de_Importacion is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Bebidas_y_tabaco is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Actividades_de_servicios_administrativos_y_de_apoyo_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Precio_de_la_onza_troy_de_plata_dolaresoz is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 60 other fieldsHigh correlation
Impuesto_al_valor_agregado is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Actividades_inmobiliarias_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Comunicaciones is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
PIB_Electricidad is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 61 other fieldsHigh correlation
Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE is highly correlated with PIB_Productos_metalicos and 54 other fieldsHigh correlation
PIB_Productos_metalicos is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Actividades_de_alojamiento_y_de_servicio_de_comidas_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Servicios_empresariales is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Construccion is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Refinacion_de_petroleo is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Celulosa is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Industrias_manufactureras_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Actividades_financieras_y_de_seguros_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Transporte_y_almacenamiento_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Comercio_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Servicios_financieros is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Otras_actividades_de_servicios_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Actividades_de_los_hogares_como_empleadores_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Explotacion_de_minas_y_canteras_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Administracion_publica is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Textil is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Otras_actividades_mineras is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Transporte is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Minerales_no_metalicos_y_metalica_basica is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Informacion_y_comunicaciones_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Administracion_publica_y_defensa_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Indice_de_produccion_industrial_electricidad__gas_y_agua is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Enseanza_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Comercio is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_a_costo_de_factores is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Mineria is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Agricultura_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Servicios_personales is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Construccion_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Actividades_artisticas_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Actividades_profesionales_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Suministro_de_electricidad_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Indice_de_ventas_comercio_real_durables_IVCM is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Suministro_de_agua_evacuacion_de_aguas_residuales_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Indice_de_ventas_comercio_real_no_durables_IVCM is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Indice_de_ventas_comercio_real_IVCM is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Mineria_del_cobre is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Maderas_y_muebles is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Quimica is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Industria_Manufacturera is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Agropecuario_silvicola is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Restaurantes_y_hoteles is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Pesca is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Actividades_de_organizaciones_y_organos_extraterritoriales_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Servicios_de_vivienda is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Alimentos is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
No_sabe__No_responde_Miles_de_personas is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Derechos_de_Importacion is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Bebidas_y_tabaco is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Actividades_de_servicios_administrativos_y_de_apoyo_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Impuesto_al_valor_agregado is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Ocupacion_en_Actividades_inmobiliarias_INE is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Comunicaciones is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
PIB_Electricidad is highly correlated with Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE and 54 other fieldsHigh correlation
Imacec_empalmado has 314 (51.1%) missing values Missing
Imacec_produccion_de_bienes has 314 (51.1%) missing values Missing
Imacec_minero has 314 (51.1%) missing values Missing
Imacec_industria has 313 (51.0%) missing values Missing
Imacec_resto_de_bienes has 314 (51.1%) missing values Missing
Imacec_comercio has 313 (51.0%) missing values Missing
Imacec_servicios has 314 (51.1%) missing values Missing
Imacec_a_costo_de_factores has 314 (51.1%) missing values Missing
Imacec_no_minero has 314 (51.1%) missing values Missing
PIB_Agropecuario_silvicola has 518 (84.4%) missing values Missing
PIB_Pesca has 519 (84.5%) missing values Missing
PIB_Mineria has 519 (84.5%) missing values Missing
PIB_Mineria_del_cobre has 519 (84.5%) missing values Missing
PIB_Otras_actividades_mineras has 518 (84.4%) missing values Missing
PIB_Industria_Manufacturera has 515 (83.9%) missing values Missing
PIB_Alimentos has 519 (84.5%) missing values Missing
PIB_Bebidas_y_tabaco has 519 (84.5%) missing values Missing
PIB_Textil has 519 (84.5%) missing values Missing
PIB_Maderas_y_muebles has 519 (84.5%) missing values Missing
PIB_Celulosa has 519 (84.5%) missing values Missing
PIB_Refinacion_de_petroleo has 519 (84.5%) missing values Missing
PIB_Quimica has 516 (84.0%) missing values Missing
PIB_Minerales_no_metalicos_y_metalica_basica has 518 (84.4%) missing values Missing
PIB_Productos_metalicos has 519 (84.5%) missing values Missing
PIB_Electricidad has 519 (84.5%) missing values Missing
PIB_Construccion has 519 (84.5%) missing values Missing
PIB_Comercio has 519 (84.5%) missing values Missing
PIB_Restaurantes_y_hoteles has 519 (84.5%) missing values Missing
PIB_Transporte has 519 (84.5%) missing values Missing
PIB_Comunicaciones has 519 (84.5%) missing values Missing
PIB_Servicios_financieros has 519 (84.5%) missing values Missing
PIB_Servicios_empresariales has 519 (84.5%) missing values Missing
PIB_Servicios_de_vivienda has 519 (84.5%) missing values Missing
PIB_Servicios_personales has 519 (84.5%) missing values Missing
PIB_Administracion_publica has 519 (84.5%) missing values Missing
PIB_a_costo_de_factores has 519 (84.5%) missing values Missing
Impuesto_al_valor_agregado has 519 (84.5%) missing values Missing
Derechos_de_Importacion has 519 (84.5%) missing values Missing
PIB has 519 (84.5%) missing values Missing
Precio_de_la_gasolina_en_EEUU_dolaresm3 has 20 (3.3%) missing values Missing
Precio_del_diesel_centavos_de_dolargalon has 442 (72.0%) missing values Missing
Precio_del_gas_natural_dolaresmillon_de_unidades_termicas_britanicas has 361 (58.8%) missing values Missing
Precio_del_petroleo_Brent_dolaresbarril has 361 (58.8%) missing values Missing
Precio_del_kerosene_dolaresm3 has 361 (58.8%) missing values Missing
Precio_del_petroleo_WTI_dolaresbarril has 161 (26.2%) missing values Missing
Precio_del_propano_centavos_de_dolargalon_DTN has 457 (74.4%) missing values Missing
Tipo_de_cambio_del_dolar_observado_diario has 152 (24.8%) missing values Missing
Ocupados has 484 (78.8%) missing values Missing
Ocupacion_en_Agricultura_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Explotacion_de_minas_y_canteras_INE has 519 (84.5%) missing values Missing
Ocupacion_en_Industrias_manufactureras_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Suministro_de_electricidad_INE has 519 (84.5%) missing values Missing
Ocupacion_en_Actividades_de_servicios_administrativos_y_de_apoyo_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Actividades_profesionales_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Actividades_inmobiliarias_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Actividades_financieras_y_de_seguros_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Informacion_y_comunicaciones_INE has 519 (84.5%) missing values Missing
Ocupacion_en_Transporte_y_almacenamiento_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Actividades_de_alojamiento_y_de_servicio_de_comidas_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Construccion_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Comercio_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Suministro_de_agua_evacuacion_de_aguas_residuales_INE has 516 (84.0%) missing values Missing
Ocupacion_en_Administracion_publica_y_defensa_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Enseanza_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Actividades_artisticas_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Otras_actividades_de_servicios_INE has 519 (84.5%) missing values Missing
Ocupacion_en_Actividades_de_los_hogares_como_empleadores_INE has 520 (84.7%) missing values Missing
Ocupacion_en_Actividades_de_organizaciones_y_organos_extraterritoriales_INE has 520 (84.7%) missing values Missing
No_sabe__No_responde_Miles_de_personas has 604 (98.4%) missing values Missing
Tipo_de_cambio_nominal_multilateral___TCM has 301 (49.0%) missing values Missing
Indice_de_tipo_de_cambio_real___TCR_promedio_1986_100 has 194 (31.6%) missing values Missing
Indice_de_produccion_industrial has 470 (76.5%) missing values Missing
Indice_de_produccion_industrial__mineria has 242 (39.4%) missing values Missing
Indice_de_produccion_industrial_electricidad__gas_y_agua has 530 (86.3%) missing values Missing
Indice_de_produccion_industrial__manufacturera has 254 (41.4%) missing values Missing
Generacion_de_energia_electrica_CDEC_GWh has 350 (57.0%) missing values Missing
Indice_de_ventas_comercio_real_IVCM has 530 (86.3%) missing values Missing
Indice_de_ventas_comercio_real_no_durables_IVCM has 530 (86.3%) missing values Missing
Indice_de_ventas_comercio_real_durables_IVCM has 529 (86.2%) missing values Missing
Ventas_autos_nuevos has 469 (76.4%) missing values Missing
Periodo is uniformly distributed Uniform
Imacec_empalmado is uniformly distributed Uniform
Imacec_produccion_de_bienes is uniformly distributed Uniform
Imacec_minero is uniformly distributed Uniform
Imacec_industria is uniformly distributed Uniform
Imacec_resto_de_bienes is uniformly distributed Uniform
Imacec_comercio is uniformly distributed Uniform
Imacec_servicios is uniformly distributed Uniform
Imacec_a_costo_de_factores is uniformly distributed Uniform
Imacec_no_minero is uniformly distributed Uniform
PIB_Agropecuario_silvicola is uniformly distributed Uniform
PIB_Pesca is uniformly distributed Uniform
PIB_Mineria is uniformly distributed Uniform
PIB_Mineria_del_cobre is uniformly distributed Uniform
PIB_Otras_actividades_mineras is uniformly distributed Uniform
PIB_Industria_Manufacturera is uniformly distributed Uniform
PIB_Alimentos is uniformly distributed Uniform
PIB_Bebidas_y_tabaco is uniformly distributed Uniform
PIB_Textil is uniformly distributed Uniform
PIB_Maderas_y_muebles is uniformly distributed Uniform
PIB_Celulosa is uniformly distributed Uniform
PIB_Refinacion_de_petroleo is uniformly distributed Uniform
PIB_Quimica is uniformly distributed Uniform
PIB_Minerales_no_metalicos_y_metalica_basica is uniformly distributed Uniform
PIB_Productos_metalicos is uniformly distributed Uniform
PIB_Electricidad is uniformly distributed Uniform
PIB_Construccion is uniformly distributed Uniform
PIB_Comercio is uniformly distributed Uniform
PIB_Restaurantes_y_hoteles is uniformly distributed Uniform
PIB_Transporte is uniformly distributed Uniform
PIB_Comunicaciones is uniformly distributed Uniform
PIB_Servicios_financieros is uniformly distributed Uniform
PIB_Servicios_empresariales is uniformly distributed Uniform
PIB_Servicios_de_vivienda is uniformly distributed Uniform
PIB_Servicios_personales is uniformly distributed Uniform
PIB_Administracion_publica is uniformly distributed Uniform
PIB_a_costo_de_factores is uniformly distributed Uniform
Impuesto_al_valor_agregado is uniformly distributed Uniform
Derechos_de_Importacion is uniformly distributed Uniform
PIB is uniformly distributed Uniform
Precio_de_la_gasolina_en_EEUU_dolaresm3 is uniformly distributed Uniform
Precio_del_cobre_refinado_BML_dolareslibra is uniformly distributed Uniform
Precio_del_kerosene_dolaresm3 is uniformly distributed Uniform
Tipo_de_cambio_del_dolar_observado_diario is uniformly distributed Uniform
Ocupados is uniformly distributed Uniform
Ocupacion_en_Agricultura_INE is uniformly distributed Uniform
Ocupacion_en_Explotacion_de_minas_y_canteras_INE is uniformly distributed Uniform
Ocupacion_en_Industrias_manufactureras_INE is uniformly distributed Uniform
Ocupacion_en_Suministro_de_electricidad_INE is uniformly distributed Uniform
Ocupacion_en_Actividades_de_servicios_administrativos_y_de_apoyo_INE is uniformly distributed Uniform
Ocupacion_en_Actividades_profesionales_INE is uniformly distributed Uniform
Ocupacion_en_Actividades_inmobiliarias_INE is uniformly distributed Uniform
Ocupacion_en_Actividades_financieras_y_de_seguros_INE is uniformly distributed Uniform
Ocupacion_en_Informacion_y_comunicaciones_INE is uniformly distributed Uniform
Ocupacion_en_Transporte_y_almacenamiento_INE is uniformly distributed Uniform
Ocupacion_en_Actividades_de_alojamiento_y_de_servicio_de_comidas_INE is uniformly distributed Uniform
Ocupacion_en_Construccion_INE is uniformly distributed Uniform
Ocupacion_en_Comercio_INE is uniformly distributed Uniform
Ocupacion_en_Suministro_de_agua_evacuacion_de_aguas_residuales_INE is uniformly distributed Uniform
Ocupacion_en_Administracion_publica_y_defensa_INE is uniformly distributed Uniform
Ocupacion_en_Enseanza_INE is uniformly distributed Uniform
Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE is uniformly distributed Uniform
Ocupacion_en_Actividades_artisticas_INE is uniformly distributed Uniform
Ocupacion_en_Otras_actividades_de_servicios_INE is uniformly distributed Uniform
Ocupacion_en_Actividades_de_los_hogares_como_empleadores_INE is uniformly distributed Uniform
Ocupacion_en_Actividades_de_organizaciones_y_organos_extraterritoriales_INE is uniformly distributed Uniform
No_sabe__No_responde_Miles_de_personas is uniformly distributed Uniform
Tipo_de_cambio_nominal_multilateral___TCM is uniformly distributed Uniform
Indice_de_tipo_de_cambio_real___TCR_promedio_1986_100 is uniformly distributed Uniform
Indice_de_produccion_industrial is uniformly distributed Uniform
Indice_de_produccion_industrial__mineria is uniformly distributed Uniform
Indice_de_produccion_industrial_electricidad__gas_y_agua is uniformly distributed Uniform
Indice_de_produccion_industrial__manufacturera is uniformly distributed Uniform
Generacion_de_energia_electrica_CDEC_GWh is uniformly distributed Uniform
Indice_de_ventas_comercio_real_IVCM is uniformly distributed Uniform
Indice_de_ventas_comercio_real_no_durables_IVCM is uniformly distributed Uniform
Indice_de_ventas_comercio_real_durables_IVCM is uniformly distributed Uniform

Reproduction

Analysis started2021-07-28 23:39:48.964720
Analysis finished2021-07-28 23:40:43.784130
Duration54.82 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Periodo
Categorical

HIGH CARDINALITY
UNIFORM

Distinct612
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2019-08-01 00:00:00 UTC
 
2
2018-08-01 00:00:00 UTC
 
2
1992-03-01 00:00:00 UTC
 
1
1991-09-01 00:00:00 UTC
 
1
1991-10-01 00:00:00 UTC
 
1
Other values (607)
607 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters14122
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique610 ?
Unique (%)99.3%

Sample

1st row2013-03-01 00:00:00 UTC
2nd row2013-04-01 00:00:00 UTC
3rd row2013-05-01 00:00:00 UTC
4th row2013-06-01 00:00:00 UTC
5th row2013-07-01 00:00:00 UTC

Common Values

ValueCountFrequency (%)
2019-08-01 00:00:00 UTC2
 
0.3%
2018-08-01 00:00:00 UTC2
 
0.3%
1992-03-01 00:00:00 UTC1
 
0.2%
1991-09-01 00:00:00 UTC1
 
0.2%
1991-10-01 00:00:00 UTC1
 
0.2%
1991-11-01 00:00:00 UTC1
 
0.2%
1991-12-01 00:00:00 UTC1
 
0.2%
1992-01-01 00:00:00 UTC1
 
0.2%
1992-02-01 00:00:00 UTC1
 
0.2%
1992-04-01 00:00:00 UTC1
 
0.2%
Other values (602)602
98.0%

Length

2021-07-28T19:40:43.986591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
utc614
33.3%
00:00:00614
33.3%
2019-08-012
 
0.1%
2018-08-012
 
0.1%
1992-03-011
 
0.1%
1991-08-011
 
0.1%
1991-09-011
 
0.1%
1991-10-011
 
0.1%
1991-11-011
 
0.1%
1991-12-011
 
0.1%
Other values (604)604
32.8%

Most occurring characters

ValueCountFrequency (%)
05256
37.2%
11411
 
10.0%
-1228
 
8.7%
1228
 
8.7%
:1228
 
8.7%
U614
 
4.3%
T614
 
4.3%
C614
 
4.3%
9592
 
4.2%
2427
 
3.0%
Other values (6)910
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8596
60.9%
Uppercase Letter1842
 
13.0%
Dash Punctuation1228
 
8.7%
Space Separator1228
 
8.7%
Other Punctuation1228
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05256
61.1%
11411
 
16.4%
9592
 
6.9%
2427
 
5.0%
8234
 
2.7%
7231
 
2.7%
3112
 
1.3%
4111
 
1.3%
5111
 
1.3%
6111
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
U614
33.3%
T614
33.3%
C614
33.3%
Dash Punctuation
ValueCountFrequency (%)
-1228
100.0%
Space Separator
ValueCountFrequency (%)
1228
100.0%
Other Punctuation
ValueCountFrequency (%)
:1228
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12280
87.0%
Latin1842
 
13.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05256
42.8%
11411
 
11.5%
-1228
 
10.0%
1228
 
10.0%
:1228
 
10.0%
9592
 
4.8%
2427
 
3.5%
8234
 
1.9%
7231
 
1.9%
3112
 
0.9%
Other values (3)333
 
2.7%
Latin
ValueCountFrequency (%)
U614
33.3%
T614
33.3%
C614
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII14122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05256
37.2%
11411
 
10.0%
-1228
 
8.7%
1228
 
8.7%
:1228
 
8.7%
U614
 
4.3%
T614
 
4.3%
C614
 
4.3%
9592
 
4.2%
2427
 
3.0%
Other values (6)910
 
6.4%

Imacec_empalmado
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct298
Distinct (%)99.3%
Missing314
Missing (%)51.1%
Memory size4.9 KiB
113.886.371
 
2
109.690.834
 
2
598.223.806
 
1
626.798.664
 
1
553.421.824
 
1
Other values (293)
293 

Length

Max length11
Median length11
Mean length10.90333333
Min length9

Characters and Unicode

Total characters3271
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)98.7%

Sample

1st row102.796.218
2nd row101.664.842
3rd row101.642.954
4th row998.310.201
5th row964.696.194

Common Values

ValueCountFrequency (%)
113.886.3712
 
0.3%
109.690.8342
 
0.3%
598.223.8061
 
0.2%
626.798.6641
 
0.2%
553.421.8241
 
0.2%
571.266.5171
 
0.2%
615.877.1821
 
0.2%
591.278.4351
 
0.2%
582.111.7381
 
0.2%
537.220.0011
 
0.2%
Other values (288)288
46.9%
(Missing)314
51.1%

Length

2021-07-28T19:40:44.200248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
113.886.3712
 
0.7%
109.690.8342
 
0.7%
626.798.6641
 
0.3%
553.421.8241
 
0.3%
571.266.5171
 
0.3%
615.877.1821
 
0.3%
591.278.4351
 
0.3%
582.111.7381
 
0.3%
537.220.0011
 
0.3%
55.488.8211
 
0.3%
Other values (288)288
96.0%

Most occurring characters

ValueCountFrequency (%)
.600
18.3%
1326
10.0%
7283
8.7%
6276
8.4%
8274
8.4%
5274
8.4%
0258
7.9%
9254
7.8%
4247
7.6%
2244
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2671
81.7%
Other Punctuation600
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1326
12.2%
7283
10.6%
6276
10.3%
8274
10.3%
5274
10.3%
0258
9.7%
9254
9.5%
4247
9.2%
2244
9.1%
3235
8.8%
Other Punctuation
ValueCountFrequency (%)
.600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.600
18.3%
1326
10.0%
7283
8.7%
6276
8.4%
8274
8.4%
5274
8.4%
0258
7.9%
9254
7.8%
4247
7.6%
2244
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.600
18.3%
1326
10.0%
7283
8.7%
6276
8.4%
8274
8.4%
5274
8.4%
0258
7.9%
9254
7.8%
4247
7.6%
2244
7.5%

Imacec_produccion_de_bienes
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct298
Distinct (%)99.3%
Missing314
Missing (%)51.1%
Memory size4.9 KiB
103.237.519
 
2
994.693.267
 
2
745.118.536
 
1
834.972.515
 
1
771.068.779
 
1
Other values (293)
293 

Length

Max length11
Median length11
Mean length10.89666667
Min length9

Characters and Unicode

Total characters3269
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)98.7%

Sample

1st row10.546.824
2nd row999.272.757
3rd row993.959.922
4th row968.367.884
5th row961.051.418

Common Values

ValueCountFrequency (%)
103.237.5192
 
0.3%
994.693.2672
 
0.3%
745.118.5361
 
0.2%
834.972.5151
 
0.2%
771.068.7791
 
0.2%
779.483.1871
 
0.2%
809.093.5731
 
0.2%
733.740.5281
 
0.2%
735.068.4341
 
0.2%
647.224.2591
 
0.2%
Other values (288)288
46.9%
(Missing)314
51.1%

Length

2021-07-28T19:40:44.421656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
103.237.5192
 
0.7%
994.693.2672
 
0.7%
834.972.5151
 
0.3%
771.068.7791
 
0.3%
779.483.1871
 
0.3%
809.093.5731
 
0.3%
733.740.5281
 
0.3%
735.068.4341
 
0.3%
647.224.2591
 
0.3%
678.466.0271
 
0.3%
Other values (288)288
96.0%

Most occurring characters

ValueCountFrequency (%)
.600
18.4%
8320
9.8%
1316
9.7%
9312
9.5%
7287
8.8%
6282
8.6%
5249
7.6%
0245
7.5%
3229
 
7.0%
2217
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2669
81.6%
Other Punctuation600
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8320
12.0%
1316
11.8%
9312
11.7%
7287
10.8%
6282
10.6%
5249
9.3%
0245
9.2%
3229
8.6%
2217
8.1%
4212
7.9%
Other Punctuation
ValueCountFrequency (%)
.600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3269
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.600
18.4%
8320
9.8%
1316
9.7%
9312
9.5%
7287
8.8%
6282
8.6%
5249
7.6%
0245
7.5%
3229
 
7.0%
2217
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.600
18.4%
8320
9.8%
1316
9.7%
9312
9.5%
7287
8.8%
6282
8.6%
5249
7.6%
0245
7.5%
3229
 
7.0%
2217
 
6.6%

Imacec_minero
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct298
Distinct (%)99.3%
Missing314
Missing (%)51.1%
Memory size4.9 KiB
106.828.407
 
2
101.027.633
 
2
974.372.253
 
1
954.612.042
 
1
840.395.598
 
1
Other values (293)
293 

Length

Max length11
Median length11
Mean length10.94
Min length10

Characters and Unicode

Total characters3282
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)98.7%

Sample

1st row978.554.365
2nd row927.333.293
3rd row96.133.164
4th row102.400.933
5th row980.029.022

Common Values

ValueCountFrequency (%)
106.828.4072
 
0.3%
101.027.6332
 
0.3%
974.372.2531
 
0.2%
954.612.0421
 
0.2%
840.395.5981
 
0.2%
888.432.9721
 
0.2%
101.312.7311
 
0.2%
970.466.4971
 
0.2%
975.775.5441
 
0.2%
923.823.5251
 
0.2%
Other values (288)288
46.9%
(Missing)314
51.1%

Length

2021-07-28T19:40:44.638155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
106.828.4072
 
0.7%
101.027.6332
 
0.7%
954.612.0421
 
0.3%
840.395.5981
 
0.3%
888.432.9721
 
0.3%
101.312.7311
 
0.3%
970.466.4971
 
0.3%
975.775.5441
 
0.3%
923.823.5251
 
0.3%
924.543.7131
 
0.3%
Other values (288)288
96.0%

Most occurring characters

ValueCountFrequency (%)
.600
18.3%
9349
10.6%
1340
10.4%
0292
8.9%
8285
8.7%
7261
8.0%
5249
7.6%
2235
 
7.2%
4229
 
7.0%
3221
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2682
81.7%
Other Punctuation600
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9349
13.0%
1340
12.7%
0292
10.9%
8285
10.6%
7261
9.7%
5249
9.3%
2235
8.8%
4229
8.5%
3221
8.2%
6221
8.2%
Other Punctuation
ValueCountFrequency (%)
.600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3282
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.600
18.3%
9349
10.6%
1340
10.4%
0292
8.9%
8285
8.7%
7261
8.0%
5249
7.6%
2235
 
7.2%
4229
 
7.0%
3221
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.600
18.3%
9349
10.6%
1340
10.4%
0292
8.9%
8285
8.7%
7261
8.0%
5249
7.6%
2235
 
7.2%
4229
 
7.0%
3221
 
6.7%

Imacec_industria
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct299
Distinct (%)99.3%
Missing313
Missing (%)51.0%
Memory size4.9 KiB
105.153.122
 
2
1.051.861
 
2
745.829.395
 
1
775.185.255
 
1
691.244.049
 
1
Other values (294)
294 

Length

Max length11
Median length11
Mean length10.84053156
Min length1

Characters and Unicode

Total characters3263
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique297 ?
Unique (%)98.7%

Sample

1st row102.297.537
2nd row104.485.589
3rd row105.445.361
4th row999.212.106
5th row100.882.112

Common Values

ValueCountFrequency (%)
105.153.1222
 
0.3%
1.051.8612
 
0.3%
745.829.3951
 
0.2%
775.185.2551
 
0.2%
691.244.0491
 
0.2%
723.284.6951
 
0.2%
766.720.2941
 
0.2%
711.111.3531
 
0.2%
756.147.3781
 
0.2%
670.375.6541
 
0.2%
Other values (289)289
47.1%
(Missing)313
51.0%

Length

2021-07-28T19:40:44.845603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
105.153.1222
 
0.7%
1.051.8612
 
0.7%
775.185.2551
 
0.3%
691.244.0491
 
0.3%
723.284.6951
 
0.3%
766.720.2941
 
0.3%
711.111.3531
 
0.3%
756.147.3781
 
0.3%
670.375.6541
 
0.3%
713.839.6221
 
0.3%
Other values (289)289
96.0%

Most occurring characters

ValueCountFrequency (%)
.600
18.4%
9323
9.9%
1308
9.4%
7297
9.1%
6281
8.6%
8272
8.3%
0261
8.0%
3247
7.6%
5240
 
7.4%
2219
 
6.7%
Other values (2)215
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2662
81.6%
Other Punctuation600
 
18.4%
Lowercase Letter1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9323
12.1%
1308
11.6%
7297
11.2%
6281
10.6%
8272
10.2%
0261
9.8%
3247
9.3%
5240
9.0%
2219
8.2%
4214
8.0%
Other Punctuation
ValueCountFrequency (%)
.600
100.0%
Lowercase Letter
ValueCountFrequency (%)
a1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3262
> 99.9%
Latin1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
.600
18.4%
9323
9.9%
1308
9.4%
7297
9.1%
6281
8.6%
8272
8.3%
0261
8.0%
3247
7.6%
5240
 
7.4%
2219
 
6.7%
Latin
ValueCountFrequency (%)
a1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.600
18.4%
9323
9.9%
1308
9.4%
7297
9.1%
6281
8.6%
8272
8.3%
0261
8.0%
3247
7.6%
5240
 
7.4%
2219
 
6.7%
Other values (2)215
 
6.6%

Imacec_resto_de_bienes
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct298
Distinct (%)99.3%
Missing314
Missing (%)51.1%
Memory size4.9 KiB
984.395.274
 
2
931.900.957
 
2
574.694.195
 
1
751.792.588
 
1
733.212.792
 
1
Other values (293)
293 

Length

Max length11
Median length11
Mean length10.90333333
Min length9

Characters and Unicode

Total characters3271
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)98.7%

Sample

1st row114.994.919
2nd row102.199.311
3rd row968.789.055
4th row89.190.493
5th row901.785.886

Common Values

ValueCountFrequency (%)
984.395.2742
 
0.3%
931.900.9572
 
0.3%
574.694.1951
 
0.2%
751.792.5881
 
0.2%
733.212.7921
 
0.2%
703.133.5671
 
0.2%
68.001.4581
 
0.2%
588.258.9061
 
0.2%
547.078.8191
 
0.2%
460.472.8421
 
0.2%
Other values (288)288
46.9%
(Missing)314
51.1%

Length

2021-07-28T19:40:45.043106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
984.395.2742
 
0.7%
931.900.9572
 
0.7%
751.792.5881
 
0.3%
733.212.7921
 
0.3%
703.133.5671
 
0.3%
68.001.4581
 
0.3%
588.258.9061
 
0.3%
547.078.8191
 
0.3%
460.472.8421
 
0.3%
483.674.2581
 
0.3%
Other values (288)288
96.0%

Most occurring characters

ValueCountFrequency (%)
.600
18.3%
9305
9.3%
1298
9.1%
7278
8.5%
8269
8.2%
2267
8.2%
6260
7.9%
3252
7.7%
4248
7.6%
5248
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2671
81.7%
Other Punctuation600
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9305
11.4%
1298
11.2%
7278
10.4%
8269
10.1%
2267
10.0%
6260
9.7%
3252
9.4%
4248
9.3%
5248
9.3%
0246
9.2%
Other Punctuation
ValueCountFrequency (%)
.600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.600
18.3%
9305
9.3%
1298
9.1%
7278
8.5%
8269
8.2%
2267
8.2%
6260
7.9%
3252
7.7%
4248
7.6%
5248
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.600
18.3%
9305
9.3%
1298
9.1%
7278
8.5%
8269
8.2%
2267
8.2%
6260
7.9%
3252
7.7%
4248
7.6%
5248
7.6%

Imacec_comercio
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct299
Distinct (%)99.3%
Missing313
Missing (%)51.0%
Memory size4.9 KiB
106.901.816
 
2
106.248.833
 
2
366.505.713
 
1
483.258.201
 
1
420.590.211
 
1
Other values (294)
294 

Length

Max length11
Median length11
Mean length10.90697674
Min length9

Characters and Unicode

Total characters3283
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique297 ?
Unique (%)98.7%

Sample

1st row110.729.395
2nd row106.098.291
3rd row100.462.117
4th row939.504.414
5th row92.776.599

Common Values

ValueCountFrequency (%)
106.901.8162
 
0.3%
106.248.8332
 
0.3%
366.505.7131
 
0.2%
483.258.2011
 
0.2%
420.590.2111
 
0.2%
432.301.6381
 
0.2%
514.891.7061
 
0.2%
396.071.5881
 
0.2%
393.282.5481
 
0.2%
374.121.9881
 
0.2%
Other values (289)289
47.1%
(Missing)313
51.0%

Length

2021-07-28T19:40:45.263244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
106.901.8162
 
0.7%
106.248.8332
 
0.7%
483.258.2011
 
0.3%
420.590.2111
 
0.3%
432.301.6381
 
0.3%
514.891.7061
 
0.3%
396.071.5881
 
0.3%
393.282.5481
 
0.3%
366.505.7131
 
0.3%
374.121.9881
 
0.3%
Other values (289)289
96.0%

Most occurring characters

ValueCountFrequency (%)
.602
18.3%
1329
10.0%
9293
8.9%
4292
8.9%
3288
8.8%
2262
8.0%
6261
8.0%
5248
7.6%
0238
 
7.2%
8237
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2681
81.7%
Other Punctuation602
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1329
12.3%
9293
10.9%
4292
10.9%
3288
10.7%
2262
9.8%
6261
9.7%
5248
9.3%
0238
8.9%
8237
8.8%
7233
8.7%
Other Punctuation
ValueCountFrequency (%)
.602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.602
18.3%
1329
10.0%
9293
8.9%
4292
8.9%
3288
8.8%
2262
8.0%
6261
8.0%
5248
7.6%
0238
 
7.2%
8237
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.602
18.3%
1329
10.0%
9293
8.9%
4292
8.9%
3288
8.8%
2262
8.0%
6261
8.0%
5248
7.6%
0238
 
7.2%
8237
 
7.2%

Imacec_servicios
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct298
Distinct (%)99.3%
Missing314
Missing (%)51.1%
Memory size4.9 KiB
122.596.004
 
2
117.404.143
 
2
547.674.008
 
1
538.302.094
 
1
456.984.963
 
1
Other values (293)
293 

Length

Max length11
Median length11
Mean length10.84666667
Min length7

Characters and Unicode

Total characters3254
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)98.7%

Sample

1st row100.064.328
2nd row102.600.107
3rd row104.083.216
4th row103.857.161
5th row969.309.272

Common Values

ValueCountFrequency (%)
122.596.0042
 
0.3%
117.404.1432
 
0.3%
547.674.0081
 
0.2%
538.302.0941
 
0.2%
456.984.9631
 
0.2%
477.847.7161
 
0.2%
516.724.6261
 
0.2%
55.030.0911
 
0.2%
529.936.9081
 
0.2%
502.610.7371
 
0.2%
Other values (288)288
46.9%
(Missing)314
51.1%

Length

2021-07-28T19:40:45.484713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
122.596.0042
 
0.7%
117.404.1432
 
0.7%
538.302.0941
 
0.3%
456.984.9631
 
0.3%
477.847.7161
 
0.3%
516.724.6261
 
0.3%
55.030.0911
 
0.3%
529.936.9081
 
0.3%
502.610.7371
 
0.3%
517.353.4231
 
0.3%
Other values (288)288
96.0%

Most occurring characters

ValueCountFrequency (%)
.598
18.4%
1320
9.8%
4298
9.2%
8296
9.1%
9266
8.2%
5261
8.0%
6253
7.8%
0251
7.7%
3247
7.6%
7246
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2656
81.6%
Other Punctuation598
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1320
12.0%
4298
11.2%
8296
11.1%
9266
10.0%
5261
9.8%
6253
9.5%
0251
9.5%
3247
9.3%
7246
9.3%
2218
8.2%
Other Punctuation
ValueCountFrequency (%)
.598
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3254
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.598
18.4%
1320
9.8%
4298
9.2%
8296
9.1%
9266
8.2%
5261
8.0%
6253
7.8%
0251
7.7%
3247
7.6%
7246
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.598
18.4%
1320
9.8%
4298
9.2%
8296
9.1%
9266
8.2%
5261
8.0%
6253
7.8%
0251
7.7%
3247
7.6%
7246
7.6%

Imacec_a_costo_de_factores
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct298
Distinct (%)99.3%
Missing314
Missing (%)51.1%
Memory size4.9 KiB
113.695.907
 
2
109.458.386
 
2
612.974.348
 
1
643.974.814
 
1
568.016.353
 
1
Other values (293)
293 

Length

Max length11
Median length11
Mean length10.89333333
Min length9

Characters and Unicode

Total characters3268
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)98.7%

Sample

1st row103.163.651
2nd row101.937.319
3rd row101.953.299
4th row100.222.381
5th row962.069.134

Common Values

ValueCountFrequency (%)
113.695.9072
 
0.3%
109.458.3862
 
0.3%
612.974.3481
 
0.2%
643.974.8141
 
0.2%
568.016.3531
 
0.2%
584.364.0441
 
0.2%
62.756.3091
 
0.2%
606.990.0751
 
0.2%
595.132.4171
 
0.2%
547.834.0341
 
0.2%
Other values (288)288
46.9%
(Missing)314
51.1%

Length

2021-07-28T19:40:45.928881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
113.695.9072
 
0.7%
109.458.3862
 
0.7%
643.974.8141
 
0.3%
568.016.3531
 
0.3%
584.364.0441
 
0.3%
62.756.3091
 
0.3%
606.990.0751
 
0.3%
595.132.4171
 
0.3%
547.834.0341
 
0.3%
567.324.5031
 
0.3%
Other values (288)288
96.0%

Most occurring characters

ValueCountFrequency (%)
.600
18.4%
1347
10.6%
5313
9.6%
9277
8.5%
8271
8.3%
6258
7.9%
0257
7.9%
7257
7.9%
3239
 
7.3%
4235
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2668
81.6%
Other Punctuation600
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1347
13.0%
5313
11.7%
9277
10.4%
8271
10.2%
6258
9.7%
0257
9.6%
7257
9.6%
3239
9.0%
4235
8.8%
2214
8.0%
Other Punctuation
ValueCountFrequency (%)
.600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3268
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.600
18.4%
1347
10.6%
5313
9.6%
9277
8.5%
8271
8.3%
6258
7.9%
0257
7.9%
7257
7.9%
3239
 
7.3%
4235
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.600
18.4%
1347
10.6%
5313
9.6%
9277
8.5%
8271
8.3%
6258
7.9%
0257
7.9%
7257
7.9%
3239
 
7.3%
4235
 
7.2%

Imacec_no_minero
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct298
Distinct (%)99.3%
Missing314
Missing (%)51.1%
Memory size4.9 KiB
114.580.893
 
2
110.578.221
 
2
55.492.608
 
1
584.215.836
 
1
515.927.418
 
1
Other values (293)
293 

Length

Max length11
Median length11
Mean length10.89666667
Min length7

Characters and Unicode

Total characters3269
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)98.7%

Sample

1st row103.405.852
2nd row102.766.884
3rd row102.322.796
4th row995.139.235
5th row962.804.305

Common Values

ValueCountFrequency (%)
114.580.8932
 
0.3%
110.578.2212
 
0.3%
55.492.6081
 
0.2%
584.215.8361
 
0.2%
515.927.4181
 
0.2%
53.168.4191
 
0.2%
57.099.4361
 
0.2%
548.264.5721
 
0.2%
539.057.4091
 
0.2%
496.676.3381
 
0.2%
Other values (288)288
46.9%
(Missing)314
51.1%

Length

2021-07-28T19:40:46.155244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
114.580.8932
 
0.7%
110.578.2212
 
0.7%
584.215.8361
 
0.3%
515.927.4181
 
0.3%
53.168.4191
 
0.3%
57.099.4361
 
0.3%
548.264.5721
 
0.3%
539.057.4091
 
0.3%
496.676.3381
 
0.3%
514.041.9231
 
0.3%
Other values (288)288
96.0%

Most occurring characters

ValueCountFrequency (%)
.599
18.3%
1336
10.3%
5299
9.1%
7269
8.2%
4263
8.0%
0261
8.0%
9261
8.0%
8256
7.8%
6252
7.7%
3241
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2670
81.7%
Other Punctuation599
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1336
12.6%
5299
11.2%
7269
10.1%
4263
9.9%
0261
9.8%
9261
9.8%
8256
9.6%
6252
9.4%
3241
9.0%
2232
8.7%
Other Punctuation
ValueCountFrequency (%)
.599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3269
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.599
18.3%
1336
10.3%
5299
9.1%
7269
8.2%
4263
8.0%
0261
8.0%
9261
8.0%
8256
7.8%
6252
7.7%
3241
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.599
18.3%
1336
10.3%
5299
9.1%
7269
8.2%
4263
8.0%
0261
8.0%
9261
8.0%
8256
7.8%
6252
7.7%
3241
7.4%

PIB_Agropecuario_silvicola
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct94
Distinct (%)97.9%
Missing518
Missing (%)84.4%
Memory size4.9 KiB
167.218.756
 
2
167.076.841
 
2
579.846.819
 
1
62.545.764
 
1
326.277.302
 
1
Other values (89)
89 

Length

Max length11
Median length11
Mean length10.79166667
Min length1

Characters and Unicode

Total characters1036
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)95.8%

Sample

1st row579.846.819
2nd row374.895.706
3rd row311.356.127
4th row246.592.082
5th row201.156.433

Common Values

ValueCountFrequency (%)
167.218.7562
 
0.3%
167.076.8412
 
0.3%
579.846.8191
 
0.2%
62.545.7641
 
0.2%
326.277.3021
 
0.2%
276.196.3651
 
0.2%
167.583.6121
 
0.2%
189.474.2891
 
0.2%
233.621.0691
 
0.2%
294.908.6211
 
0.2%
Other values (84)84
 
13.7%
(Missing)518
84.4%

Length

2021-07-28T19:40:46.367681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
167.218.7562
 
2.1%
167.076.8412
 
2.1%
579.846.8191
 
1.0%
631.333.4531
 
1.0%
326.277.3021
 
1.0%
276.196.3651
 
1.0%
167.583.6121
 
1.0%
189.474.2891
 
1.0%
233.621.0691
 
1.0%
294.908.6211
 
1.0%
Other values (84)84
87.5%

Most occurring characters

ValueCountFrequency (%)
.190
18.3%
6121
11.7%
194
9.1%
294
9.1%
885
8.2%
784
8.1%
384
8.1%
483
8.0%
576
 
7.3%
974
 
7.1%
Other values (2)51
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number845
81.6%
Other Punctuation190
 
18.3%
Lowercase Letter1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6121
14.3%
194
11.1%
294
11.1%
885
10.1%
784
9.9%
384
9.9%
483
9.8%
576
9.0%
974
8.8%
050
5.9%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%
Lowercase Letter
ValueCountFrequency (%)
a1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1035
99.9%
Latin1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
6121
11.7%
194
9.1%
294
9.1%
885
8.2%
784
8.1%
384
8.1%
483
8.0%
576
 
7.3%
974
 
7.1%
Latin
ValueCountFrequency (%)
a1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.3%
6121
11.7%
194
9.1%
294
9.1%
885
8.2%
784
8.1%
384
8.1%
483
8.0%
576
 
7.3%
974
 
7.1%
Other values (2)51
 
4.9%

PIB_Pesca
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
618.107.584
 
2
558.930.237
 
2
570.624.122
 
1
502.195.647
 
1
704.726.326
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.88421053
Min length10

Characters and Unicode

Total characters1034
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row570.624.122
2nd row606.504.665
3rd row545.573.168
4th row418.668.119
5th row565.897.618

Common Values

ValueCountFrequency (%)
618.107.5842
 
0.3%
558.930.2372
 
0.3%
570.624.1221
 
0.2%
502.195.6471
 
0.2%
704.726.3261
 
0.2%
498.075.8341
 
0.2%
692.796.7941
 
0.2%
694.586.9471
 
0.2%
888.830.4661
 
0.2%
93.757.5541
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:46.574160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
618.107.5842
 
2.1%
558.930.2372
 
2.1%
570.624.1221
 
1.1%
502.195.6471
 
1.1%
704.726.3261
 
1.1%
498.075.8341
 
1.1%
692.796.7941
 
1.1%
694.586.9471
 
1.1%
888.830.4661
 
1.1%
93.757.5541
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
6104
10.1%
5101
9.8%
491
8.8%
790
8.7%
887
8.4%
981
7.8%
378
7.5%
174
 
7.2%
072
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number844
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6104
12.3%
5101
12.0%
491
10.8%
790
10.7%
887
10.3%
981
9.6%
378
9.2%
174
8.8%
072
8.5%
266
7.8%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1034
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
6104
10.1%
5101
9.8%
491
8.8%
790
8.7%
887
8.4%
981
7.8%
378
7.5%
174
 
7.2%
072
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
6104
10.1%
5101
9.8%
491
8.8%
790
8.7%
887
8.4%
981
7.8%
378
7.5%
174
 
7.2%
072
 
7.0%

PIB_Mineria
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
127.494.583
 
2
134.815.028
 
2
123.491.343
 
1
120.293.825
 
1
125.297.185
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.87368421
Min length9

Characters and Unicode

Total characters1033
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row123.491.343
2nd row11.702.736
3rd row121.317.874
4th row129.227.656
5th row123.677.441

Common Values

ValueCountFrequency (%)
127.494.5832
 
0.3%
134.815.0282
 
0.3%
123.491.3431
 
0.2%
120.293.8251
 
0.2%
125.297.1851
 
0.2%
129.383.0431
 
0.2%
133.249.8181
 
0.2%
13.057.3361
 
0.2%
137.448.7831
 
0.2%
124.385.2371
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:46.786158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
127.494.5832
 
2.1%
134.815.0282
 
2.1%
123.491.3431
 
1.1%
120.293.8251
 
1.1%
125.297.1851
 
1.1%
129.383.0431
 
1.1%
133.249.8181
 
1.1%
13.057.3361
 
1.1%
137.448.7831
 
1.1%
124.385.2371
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
1158
15.3%
2113
10.9%
3104
10.1%
482
7.9%
882
7.9%
771
 
6.9%
963
 
6.1%
659
 
5.7%
056
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number843
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1158
18.7%
2113
13.4%
3104
12.3%
482
9.7%
882
9.7%
771
8.4%
963
 
7.5%
659
 
7.0%
056
 
6.6%
555
 
6.5%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
1158
15.3%
2113
10.9%
3104
10.1%
482
7.9%
882
7.9%
771
 
6.9%
963
 
6.1%
659
 
5.7%
056
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
1158
15.3%
2113
10.9%
3104
10.1%
482
7.9%
882
7.9%
771
 
6.9%
963
 
6.1%
659
 
5.7%
056
 
5.4%

PIB_Mineria_del_cobre
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
114.044.798
 
2
121.692.348
 
2
110.356.254
 
1
108.961.844
 
1
110.542.041
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.92631579
Min length10

Characters and Unicode

Total characters1038
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row110.356.254
2nd row10.358.806
3rd row107.784.409
4th row115.404.932
5th row10.915.864

Common Values

ValueCountFrequency (%)
114.044.7982
 
0.3%
121.692.3482
 
0.3%
110.356.2541
 
0.2%
108.961.8441
 
0.2%
110.542.0411
 
0.2%
115.237.1831
 
0.2%
120.903.5841
 
0.2%
118.549.9251
 
0.2%
125.294.3941
 
0.2%
112.968.8881
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:46.982686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
114.044.7982
 
2.1%
121.692.3482
 
2.1%
110.356.2541
 
1.1%
108.961.8441
 
1.1%
110.542.0411
 
1.1%
115.237.1831
 
1.1%
120.903.5841
 
1.1%
118.549.9251
 
1.1%
125.294.3941
 
1.1%
112.968.8881
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
1200
19.3%
.190
18.3%
286
8.3%
083
8.0%
479
 
7.6%
977
 
7.4%
572
 
6.9%
868
 
6.6%
365
 
6.3%
660
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number848
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1200
23.6%
286
10.1%
083
9.8%
479
 
9.3%
977
 
9.1%
572
 
8.5%
868
 
8.0%
365
 
7.7%
660
 
7.1%
758
 
6.8%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1200
19.3%
.190
18.3%
286
8.3%
083
8.0%
479
 
7.6%
977
 
7.4%
572
 
6.9%
868
 
6.6%
365
 
6.3%
660
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1200
19.3%
.190
18.3%
286
8.3%
083
8.0%
479
 
7.6%
977
 
7.4%
572
 
6.9%
868
 
6.6%
365
 
6.3%
660
 
5.8%

PIB_Otras_actividades_mineras
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct94
Distinct (%)97.9%
Missing518
Missing (%)84.4%
Memory size4.9 KiB
135.911.049
 
2
132.265.047
 
2
131.350.889
 
1
123.716.785
 
1
143.891.147
 
1
Other values (89)
89 

Length

Max length11
Median length11
Mean length10.9375
Min length9

Characters and Unicode

Total characters1050
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)95.8%

Sample

1st row131.350.889
2nd row134.393.001
3rd row135.334.655
4th row138.227.237
5th row145.188.012

Common Values

ValueCountFrequency (%)
135.911.0492
 
0.3%
132.265.0472
 
0.3%
131.350.8891
 
0.2%
123.716.7851
 
0.2%
143.891.1471
 
0.2%
149.705.0461
 
0.2%
14.313.9911
 
0.2%
124.088.2211
 
0.2%
120.810.4361
 
0.2%
121.900.4731
 
0.2%
Other values (84)84
 
13.7%
(Missing)518
84.4%

Length

2021-07-28T19:40:47.184954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
135.911.0492
 
2.1%
132.265.0472
 
2.1%
131.350.8891
 
1.0%
106.097.1911
 
1.0%
143.891.1471
 
1.0%
149.705.0461
 
1.0%
14.313.9911
 
1.0%
124.088.2211
 
1.0%
120.810.4361
 
1.0%
121.900.4731
 
1.0%
Other values (84)84
87.5%

Most occurring characters

ValueCountFrequency (%)
.192
18.3%
1181
17.2%
3109
10.4%
293
8.9%
479
7.5%
973
 
7.0%
570
 
6.7%
066
 
6.3%
766
 
6.3%
661
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number858
81.7%
Other Punctuation192
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1181
21.1%
3109
12.7%
293
10.8%
479
9.2%
973
8.5%
570
 
8.2%
066
 
7.7%
766
 
7.7%
661
 
7.1%
860
 
7.0%
Other Punctuation
ValueCountFrequency (%)
.192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.192
18.3%
1181
17.2%
3109
10.4%
293
8.9%
479
7.5%
973
 
7.0%
570
 
6.7%
066
 
6.3%
766
 
6.3%
661
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.192
18.3%
1181
17.2%
3109
10.4%
293
8.9%
479
7.5%
973
 
7.0%
570
 
6.7%
066
 
6.3%
766
 
6.3%
661
 
5.8%

PIB_Industria_Manufacturera
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct94
Distinct (%)94.9%
Missing515
Missing (%)83.9%
Memory size4.9 KiB
a
 
4
134.337.385
 
2
134.295.268
 
2
136.763.201
 
1
135.923.432
 
1
Other values (89)
89 

Length

Max length11
Median length11
Mean length10.48484848
Min length1

Characters and Unicode

Total characters1038
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)91.9%

Sample

1st row130.648.285
2nd row133.442.735
3rd row134.668.498
4th row127.613.384
5th row128.840.589

Common Values

ValueCountFrequency (%)
a4
 
0.7%
134.337.3852
 
0.3%
134.295.2682
 
0.3%
136.763.2011
 
0.2%
135.923.4321
 
0.2%
140.139.5141
 
0.2%
119.526.3441
 
0.2%
128.354.4831
 
0.2%
135.194.1721
 
0.2%
139.300.4351
 
0.2%
Other values (84)84
 
13.7%
(Missing)515
83.9%

Length

2021-07-28T19:40:47.395391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a4
 
4.0%
134.337.3852
 
2.0%
134.295.2682
 
2.0%
13.639.4561
 
1.0%
135.923.4321
 
1.0%
140.139.5141
 
1.0%
119.526.3441
 
1.0%
128.354.4831
 
1.0%
135.194.1721
 
1.0%
139.300.4351
 
1.0%
Other values (84)84
84.8%

Most occurring characters

ValueCountFrequency (%)
.189
18.2%
1165
15.9%
2118
11.4%
3115
11.1%
479
7.6%
879
7.6%
569
 
6.6%
962
 
6.0%
759
 
5.7%
655
 
5.3%
Other values (2)48
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number845
81.4%
Other Punctuation189
 
18.2%
Lowercase Letter4
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1165
19.5%
2118
14.0%
3115
13.6%
479
9.3%
879
9.3%
569
8.2%
962
 
7.3%
759
 
7.0%
655
 
6.5%
044
 
5.2%
Other Punctuation
ValueCountFrequency (%)
.189
100.0%
Lowercase Letter
ValueCountFrequency (%)
a4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1034
99.6%
Latin4
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
.189
18.3%
1165
16.0%
2118
11.4%
3115
11.1%
479
7.6%
879
7.6%
569
 
6.7%
962
 
6.0%
759
 
5.7%
655
 
5.3%
Latin
ValueCountFrequency (%)
a4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.189
18.2%
1165
15.9%
2118
11.4%
3115
11.1%
479
7.6%
879
7.6%
569
 
6.6%
962
 
6.0%
759
 
5.7%
655
 
5.3%
Other values (2)48
 
4.6%

PIB_Alimentos
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
352.713.059
 
2
353.208.152
 
2
367.818.436
 
1
332.018.501
 
1
380.440.178
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.85263158
Min length9

Characters and Unicode

Total characters1031
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row367.818.436
2nd row38.211.344
3rd row37.345.015
4th row323.857.815
5th row327.206.582

Common Values

ValueCountFrequency (%)
352.713.0592
 
0.3%
353.208.1522
 
0.3%
367.818.4361
 
0.2%
332.018.5011
 
0.2%
380.440.1781
 
0.2%
301.564.8921
 
0.2%
343.457.8381
 
0.2%
364.609.2681
 
0.2%
389.355.9871
 
0.2%
37.402.9251
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:47.606826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
352.713.0592
 
2.1%
353.208.1522
 
2.1%
367.818.4361
 
1.1%
332.018.5011
 
1.1%
380.440.1781
 
1.1%
301.564.8921
 
1.1%
343.457.8381
 
1.1%
364.609.2681
 
1.1%
389.355.9871
 
1.1%
37.402.9251
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
3174
16.9%
282
8.0%
582
8.0%
881
7.9%
475
 
7.3%
671
 
6.9%
771
 
6.9%
970
 
6.8%
169
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3174
20.7%
282
9.8%
582
9.8%
881
9.6%
475
8.9%
671
8.4%
771
8.4%
970
8.3%
169
 
8.2%
066
 
7.8%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
3174
16.9%
282
8.0%
582
8.0%
881
7.9%
475
 
7.3%
671
 
6.9%
771
 
6.9%
970
 
6.8%
169
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
3174
16.9%
282
8.0%
582
8.0%
881
7.9%
475
 
7.3%
671
 
6.9%
771
 
6.9%
970
 
6.8%
169
 
6.7%

PIB_Bebidas_y_tabaco
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
155.499.282
 
2
154.888.657
 
2
162.692.807
 
1
163.596.058
 
1
16.942.394
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.87368421
Min length9

Characters and Unicode

Total characters1033
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row162.692.807
2nd row188.189.562
3rd row184.115.565
4th row17.224.193
5th row163.572.934

Common Values

ValueCountFrequency (%)
155.499.2822
 
0.3%
154.888.6572
 
0.3%
162.692.8071
 
0.2%
163.596.0581
 
0.2%
16.942.3941
 
0.2%
155.860.6991
 
0.2%
155.687.6921
 
0.2%
182.359.6671
 
0.2%
19.690.6991
 
0.2%
194.452.6971
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:47.833221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
155.499.2822
 
2.1%
154.888.6572
 
2.1%
162.692.8071
 
1.1%
163.596.0581
 
1.1%
16.942.3941
 
1.1%
155.860.6991
 
1.1%
155.687.6921
 
1.1%
182.359.6671
 
1.1%
19.690.6991
 
1.1%
194.452.6971
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
1163
15.8%
595
9.2%
691
8.8%
779
7.6%
277
7.5%
875
 
7.3%
474
 
7.2%
973
 
7.1%
363
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number843
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1163
19.3%
595
11.3%
691
10.8%
779
9.4%
277
9.1%
875
8.9%
474
8.8%
973
8.7%
363
 
7.5%
053
 
6.3%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
1163
15.8%
595
9.2%
691
8.8%
779
7.6%
277
7.5%
875
 
7.3%
474
 
7.2%
973
 
7.1%
363
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
1163
15.8%
595
9.2%
691
8.8%
779
7.6%
277
7.5%
875
 
7.3%
474
 
7.2%
973
 
7.1%
363
 
6.1%

PIB_Textil
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
282.857.251
 
2
256.242.589
 
2
330.305.467
 
1
319.287.288
 
1
328.198.517
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.92631579
Min length10

Characters and Unicode

Total characters1038
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row330.305.467
2nd row380.345.362
3rd row342.850.811
4th row290.637.238
5th row277.405.414

Common Values

ValueCountFrequency (%)
282.857.2512
 
0.3%
256.242.5892
 
0.3%
330.305.4671
 
0.2%
319.287.2881
 
0.2%
328.198.5171
 
0.2%
252.305.0871
 
0.2%
265.345.8911
 
0.2%
308.925.9131
 
0.2%
338.830.8921
 
0.2%
370.841.8711
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:48.046650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
282.857.2512
 
2.1%
256.242.5892
 
2.1%
330.305.4671
 
1.1%
319.287.2881
 
1.1%
328.198.5171
 
1.1%
252.305.0871
 
1.1%
265.345.8911
 
1.1%
308.925.9131
 
1.1%
338.830.8921
 
1.1%
370.841.8711
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.3%
2129
12.4%
3120
11.6%
895
9.2%
182
7.9%
579
7.6%
677
7.4%
771
 
6.8%
467
 
6.5%
064
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number848
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2129
15.2%
3120
14.2%
895
11.2%
182
9.7%
579
9.3%
677
9.1%
771
8.4%
467
7.9%
064
7.5%
964
7.5%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.3%
2129
12.4%
3120
11.6%
895
9.2%
182
7.9%
579
7.6%
677
7.4%
771
 
6.8%
467
 
6.5%
064
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.3%
2129
12.4%
3120
11.6%
895
9.2%
182
7.9%
579
7.6%
677
7.4%
771
 
6.8%
467
 
6.5%
064
 
6.2%

PIB_Maderas_y_muebles
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
761.910.666
 
2
728.401.461
 
2
643.366.109
 
1
69.530.084
 
1
811.197.543
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.83157895
Min length9

Characters and Unicode

Total characters1029
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row643.366.109
2nd row684.106.836
3rd row686.925.227
4th row667.221.833
5th row692.167.097

Common Values

ValueCountFrequency (%)
761.910.6662
 
0.3%
728.401.4612
 
0.3%
643.366.1091
 
0.2%
69.530.0841
 
0.2%
811.197.5431
 
0.2%
640.941.7521
 
0.2%
726.101.3831
 
0.2%
731.508.8471
 
0.2%
723.293.9461
 
0.2%
725.802.7481
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:48.263074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
761.910.6662
 
2.1%
728.401.4612
 
2.1%
643.366.1091
 
1.1%
69.530.0841
 
1.1%
811.197.5431
 
1.1%
640.941.7521
 
1.1%
726.101.3831
 
1.1%
731.508.8471
 
1.1%
723.293.9461
 
1.1%
725.802.7481
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.5%
6133
12.9%
7109
10.6%
192
8.9%
276
 
7.4%
974
 
7.2%
373
 
7.1%
573
 
7.1%
071
 
6.9%
469
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number839
81.5%
Other Punctuation190
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6133
15.9%
7109
13.0%
192
11.0%
276
9.1%
974
8.8%
373
8.7%
573
8.7%
071
8.5%
469
8.2%
869
8.2%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1029
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.5%
6133
12.9%
7109
10.6%
192
8.9%
276
 
7.4%
974
 
7.2%
373
 
7.1%
573
 
7.1%
071
 
6.9%
469
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.5%
6133
12.9%
7109
10.6%
192
8.9%
276
 
7.4%
974
 
7.2%
373
 
7.1%
573
 
7.1%
071
 
6.9%
469
 
6.7%

PIB_Celulosa
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
10.666.085
 
2
107.719.529
 
2
974.948.182
 
1
969.801.622
 
1
103.905.471
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.82105263
Min length7

Characters and Unicode

Total characters1028
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row974.948.182
2nd row945.996.965
3rd row104.685.345
4th row102.091.894
5th row10.693.519

Common Values

ValueCountFrequency (%)
10.666.0852
 
0.3%
107.719.5292
 
0.3%
974.948.1821
 
0.2%
969.801.6221
 
0.2%
103.905.4711
 
0.2%
101.982.3111
 
0.2%
105.004.5281
 
0.2%
104.804.9231
 
0.2%
107.832.3381
 
0.2%
1.034.8241
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:48.486908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.666.0852
 
2.1%
107.719.5292
 
2.1%
974.948.1821
 
1.1%
969.801.6221
 
1.1%
103.905.4711
 
1.1%
101.982.3111
 
1.1%
105.004.5281
 
1.1%
104.804.9231
 
1.1%
107.832.3381
 
1.1%
1.034.8241
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.189
18.4%
1121
11.8%
0121
11.8%
9110
10.7%
475
 
7.3%
874
 
7.2%
274
 
7.2%
771
 
6.9%
671
 
6.9%
363
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number839
81.6%
Other Punctuation189
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1121
14.4%
0121
14.4%
9110
13.1%
475
8.9%
874
8.8%
274
8.8%
771
8.5%
671
8.5%
363
7.5%
559
7.0%
Other Punctuation
ValueCountFrequency (%)
.189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1028
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.189
18.4%
1121
11.8%
0121
11.8%
9110
10.7%
475
 
7.3%
874
 
7.2%
274
 
7.2%
771
 
6.9%
671
 
6.9%
363
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.189
18.4%
1121
11.8%
0121
11.8%
9110
10.7%
475
 
7.3%
874
 
7.2%
274
 
7.2%
771
 
6.9%
671
 
6.9%
363
 
6.1%

PIB_Refinacion_de_petroleo
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
113.232.043
 
2
120.020.403
 
2
103.469.519
 
1
982.275.544
 
1
10.338.753
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.90526316
Min length9

Characters and Unicode

Total characters1036
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row103.469.519
2nd row824.530.386
3rd row101.629.225
4th row100.279.607
5th row102.108.993

Common Values

ValueCountFrequency (%)
113.232.0432
 
0.3%
120.020.4032
 
0.3%
103.469.5191
 
0.2%
982.275.5441
 
0.2%
10.338.7531
 
0.2%
104.841.8961
 
0.2%
105.897.2011
 
0.2%
103.010.3411
 
0.2%
948.526.3591
 
0.2%
107.493.8431
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:48.697498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
113.232.0432
 
2.1%
120.020.4032
 
2.1%
103.469.5191
 
1.1%
982.275.5441
 
1.1%
10.338.7531
 
1.1%
104.841.8961
 
1.1%
105.897.2011
 
1.1%
103.010.3411
 
1.1%
948.526.3591
 
1.1%
107.493.8431
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.3%
1147
14.2%
996
9.3%
094
9.1%
283
8.0%
676
 
7.3%
876
 
7.3%
373
 
7.0%
772
 
6.9%
568
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number846
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1147
17.4%
996
11.3%
094
11.1%
283
9.8%
676
9.0%
876
9.0%
373
8.6%
772
8.5%
568
8.0%
461
7.2%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.3%
1147
14.2%
996
9.3%
094
9.1%
283
8.0%
676
 
7.3%
876
 
7.3%
373
 
7.0%
772
 
6.9%
568
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.3%
1147
14.2%
996
9.3%
094
9.1%
283
8.0%
676
 
7.3%
876
 
7.3%
373
 
7.0%
772
 
6.9%
568
 
6.6%

PIB_Quimica
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct94
Distinct (%)95.9%
Missing516
Missing (%)84.0%
Memory size4.9 KiB
a
 
3
205.964.209
 
2
201.510.245
 
2
191.109.563
 
1
205.330.168
 
1
Other values (89)
89 

Length

Max length11
Median length11
Mean length10.60204082
Min length1

Characters and Unicode

Total characters1039
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)92.9%

Sample

1st row184.460.073
2nd row172.638.981
3rd row173.785.919
4th row187.920.877
5th row179.849.331

Common Values

ValueCountFrequency (%)
a3
 
0.5%
205.964.2092
 
0.3%
201.510.2452
 
0.3%
191.109.5631
 
0.2%
205.330.1681
 
0.2%
211.659.5221
 
0.2%
178.232.9651
 
0.2%
185.304.6181
 
0.2%
191.423.0471
 
0.2%
188.068.2411
 
0.2%
Other values (84)84
 
13.7%
(Missing)516
84.0%

Length

2021-07-28T19:40:48.911433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a3
 
3.1%
205.964.2092
 
2.0%
201.510.2452
 
2.0%
191.437.0751
 
1.0%
205.330.1681
 
1.0%
211.659.5221
 
1.0%
178.232.9651
 
1.0%
185.304.6181
 
1.0%
191.423.0471
 
1.0%
188.068.2411
 
1.0%
Other values (84)84
85.7%

Most occurring characters

ValueCountFrequency (%)
.190
18.3%
1153
14.7%
997
9.3%
291
8.8%
887
8.4%
786
8.3%
078
7.5%
569
 
6.6%
364
 
6.2%
462
 
6.0%
Other values (2)62
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number846
81.4%
Other Punctuation190
 
18.3%
Lowercase Letter3
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1153
18.1%
997
11.5%
291
10.8%
887
10.3%
786
10.2%
078
9.2%
569
8.2%
364
7.6%
462
7.3%
659
 
7.0%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%
Lowercase Letter
ValueCountFrequency (%)
a3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1036
99.7%
Latin3
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.3%
1153
14.8%
997
9.4%
291
8.8%
887
8.4%
786
8.3%
078
7.5%
569
 
6.7%
364
 
6.2%
462
 
6.0%
Latin
ValueCountFrequency (%)
a3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.3%
1153
14.7%
997
9.3%
291
8.8%
887
8.4%
786
8.3%
078
7.5%
569
 
6.6%
364
 
6.2%
462
 
6.0%
Other values (2)62
 
6.0%

PIB_Minerales_no_metalicos_y_metalica_basica
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct94
Distinct (%)97.9%
Missing518
Missing (%)84.4%
Memory size4.9 KiB
753.121.123
 
2
733.229.359
 
2
807.709.837
 
1
718.676.503
 
1
731.206.649
 
1
Other values (89)
89 

Length

Max length11
Median length11
Mean length10.70833333
Min length1

Characters and Unicode

Total characters1028
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)95.8%

Sample

1st row807.709.837
2nd row831.959.074
3rd row778.426.782
4th row716.634.523
5th row763.622.688

Common Values

ValueCountFrequency (%)
753.121.1232
 
0.3%
733.229.3592
 
0.3%
807.709.8371
 
0.2%
718.676.5031
 
0.2%
731.206.6491
 
0.2%
809.769.3211
 
0.2%
633.005.8961
 
0.2%
70.824.1711
 
0.2%
70.583.4831
 
0.2%
70.793.4541
 
0.2%
Other values (84)84
 
13.7%
(Missing)518
84.4%

Length

2021-07-28T19:40:49.104916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
753.121.1232
 
2.1%
733.229.3592
 
2.1%
807.709.8371
 
1.0%
689.261.9851
 
1.0%
731.206.6491
 
1.0%
809.769.3211
 
1.0%
633.005.8961
 
1.0%
70.824.1711
 
1.0%
70.583.4831
 
1.0%
70.793.4541
 
1.0%
Other values (84)84
87.5%

Most occurring characters

ValueCountFrequency (%)
.190
18.5%
7134
13.0%
6114
11.1%
390
8.8%
882
8.0%
282
8.0%
173
 
7.1%
972
 
7.0%
568
 
6.6%
062
 
6.0%
Other values (2)61
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number837
81.4%
Other Punctuation190
 
18.5%
Lowercase Letter1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7134
16.0%
6114
13.6%
390
10.8%
882
9.8%
282
9.8%
173
8.7%
972
8.6%
568
8.1%
062
7.4%
460
7.2%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%
Lowercase Letter
ValueCountFrequency (%)
a1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1027
99.9%
Latin1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.5%
7134
13.0%
6114
11.1%
390
8.8%
882
8.0%
282
8.0%
173
 
7.1%
972
 
7.0%
568
 
6.6%
062
 
6.0%
Latin
ValueCountFrequency (%)
a1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.5%
7134
13.0%
6114
11.1%
390
8.8%
882
8.0%
282
8.0%
173
 
7.1%
972
 
7.0%
568
 
6.6%
062
 
6.0%
Other values (2)61
 
5.9%

PIB_Productos_metalicos
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
229.592.623
 
2
232.194.383
 
2
212.409.058
 
1
196.721.554
 
1
239.316.092
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.92631579
Min length10

Characters and Unicode

Total characters1038
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row212.409.058
2nd row224.791.502
3rd row228.198.491
4th row222.292.356
5th row23.541.334

Common Values

ValueCountFrequency (%)
229.592.6232
 
0.3%
232.194.3832
 
0.3%
212.409.0581
 
0.2%
196.721.5541
 
0.2%
239.316.0921
 
0.2%
198.015.6671
 
0.2%
213.454.0111
 
0.2%
227.005.6291
 
0.2%
231.899.3861
 
0.2%
227.231.6461
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:49.308371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
229.592.6232
 
2.1%
232.194.3832
 
2.1%
212.409.0581
 
1.1%
196.721.5541
 
1.1%
239.316.0921
 
1.1%
198.015.6671
 
1.1%
213.454.0111
 
1.1%
227.005.6291
 
1.1%
231.899.3861
 
1.1%
227.231.6461
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.3%
2182
17.5%
1102
9.8%
988
8.5%
377
7.4%
574
 
7.1%
072
 
6.9%
470
 
6.7%
662
 
6.0%
761
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number848
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2182
21.5%
1102
12.0%
988
10.4%
377
9.1%
574
8.7%
072
 
8.5%
470
 
8.3%
662
 
7.3%
761
 
7.2%
860
 
7.1%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.3%
2182
17.5%
1102
9.8%
988
8.5%
377
7.4%
574
 
7.1%
072
 
6.9%
470
 
6.7%
662
 
6.0%
761
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.3%
2182
17.5%
1102
9.8%
988
8.5%
377
7.4%
574
 
7.1%
072
 
6.9%
470
 
6.7%
662
 
6.0%
761
 
5.9%

PIB_Electricidad
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
341.668.896
 
2
358.780.323
 
2
301.314.934
 
1
333.284.191
 
1
370.736.322
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.88421053
Min length9

Characters and Unicode

Total characters1034
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row301.314.934
2nd row280.438.294
3rd row274.899.817
4th row270.141.266
5th row293.379.905

Common Values

ValueCountFrequency (%)
341.668.8962
 
0.3%
358.780.3232
 
0.3%
301.314.9341
 
0.2%
333.284.1911
 
0.2%
370.736.3221
 
0.2%
33.177.9181
 
0.2%
361.711.7051
 
0.2%
341.750.5161
 
0.2%
341.203.2341
 
0.2%
33.084.5411
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:49.520803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
341.668.8962
 
2.1%
358.780.3232
 
2.1%
301.314.9341
 
1.1%
333.284.1911
 
1.1%
370.736.3221
 
1.1%
33.177.9181
 
1.1%
361.711.7051
 
1.1%
341.750.5161
 
1.1%
341.203.2341
 
1.1%
33.084.5411
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
3157
15.2%
191
8.8%
287
8.4%
984
8.1%
882
7.9%
779
7.6%
576
 
7.4%
469
 
6.7%
060
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number844
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3157
18.6%
191
10.8%
287
10.3%
984
10.0%
882
9.7%
779
9.4%
576
9.0%
469
8.2%
060
 
7.1%
659
 
7.0%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1034
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
3157
15.2%
191
8.8%
287
8.4%
984
8.1%
882
7.9%
779
7.6%
576
 
7.4%
469
 
6.7%
060
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
3157
15.2%
191
8.8%
287
8.4%
984
8.1%
882
7.9%
779
7.6%
576
 
7.4%
469
 
6.7%
060
 
5.8%

PIB_Construccion
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
771.082.628
 
2
818.881.759
 
2
710.242.818
 
1
670.156.799
 
1
821.761.124
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.90526316
Min length10

Characters and Unicode

Total characters1036
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row710.242.818
2nd row749.055.837
3rd row74.795.834
4th row719.957.056
5th row741.595.595

Common Values

ValueCountFrequency (%)
771.082.6282
 
0.3%
818.881.7592
 
0.3%
710.242.8181
 
0.2%
670.156.7991
 
0.2%
821.761.1241
 
0.2%
69.156.5831
 
0.2%
763.858.0961
 
0.2%
732.109.3181
 
0.2%
757.024.0151
 
0.2%
771.052.9791
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:49.731240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
771.082.6282
 
2.1%
818.881.7592
 
2.1%
710.242.8181
 
1.1%
670.156.7991
 
1.1%
821.761.1241
 
1.1%
69.156.5831
 
1.1%
763.858.0961
 
1.1%
732.109.3181
 
1.1%
757.024.0151
 
1.1%
771.052.9791
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.3%
7114
11.0%
895
9.2%
290
8.7%
189
8.6%
689
8.6%
583
8.0%
478
7.5%
972
 
6.9%
071
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number846
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7114
13.5%
895
11.2%
290
10.6%
189
10.5%
689
10.5%
583
9.8%
478
9.2%
972
8.5%
071
8.4%
365
7.7%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.3%
7114
11.0%
895
9.2%
290
8.7%
189
8.6%
689
8.6%
583
8.0%
478
7.5%
972
 
6.9%
071
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.3%
7114
11.0%
895
9.2%
290
8.7%
189
8.6%
689
8.6%
583
8.0%
478
7.5%
972
 
6.9%
071
 
6.9%

PIB_Comercio
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
110.815.907
 
2
111.496.958
 
2
115.489.064
 
1
11.850.048
 
1
11.370.537
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.8
Min length7

Characters and Unicode

Total characters1026
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row115.489.064
2nd row110.658.893
3rd row10.478.045
4th row979.888.723
5th row967.645.727

Common Values

ValueCountFrequency (%)
110.815.9072
 
0.3%
111.496.9582
 
0.3%
115.489.0641
 
0.2%
11.850.0481
 
0.2%
11.370.5371
 
0.2%
10.354.1071
 
0.2%
105.606.3511
 
0.2%
112.070.7921
 
0.2%
119.229.6941
 
0.2%
123.406.2941
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:49.951055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
110.815.9072
 
2.1%
111.496.9582
 
2.1%
115.489.0641
 
1.1%
11.850.0481
 
1.1%
11.370.5371
 
1.1%
10.354.1071
 
1.1%
105.606.3511
 
1.1%
112.070.7921
 
1.1%
119.229.6941
 
1.1%
123.406.2941
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.189
18.4%
1161
15.7%
0101
9.8%
982
8.0%
278
7.6%
675
 
7.3%
471
 
6.9%
869
 
6.7%
369
 
6.7%
767
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number837
81.6%
Other Punctuation189
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1161
19.2%
0101
12.1%
982
9.8%
278
9.3%
675
9.0%
471
8.5%
869
8.2%
369
8.2%
767
8.0%
564
 
7.6%
Other Punctuation
ValueCountFrequency (%)
.189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1026
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.189
18.4%
1161
15.7%
0101
9.8%
982
8.0%
278
7.6%
675
 
7.3%
471
 
6.9%
869
 
6.7%
369
 
6.7%
767
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.189
18.4%
1161
15.7%
0101
9.8%
982
8.0%
278
7.6%
675
 
7.3%
471
 
6.9%
869
 
6.7%
369
 
6.7%
767
 
6.5%

PIB_Restaurantes_y_hoteles
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
240.435.294
 
2
25.457.964
 
2
202.838.669
 
1
244.101.305
 
1
252.061.229
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.86315789
Min length7

Characters and Unicode

Total characters1032
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row202.838.669
2nd row209.310.713
3rd row206.481.563
4th row208.021.235
5th row212.827.237

Common Values

ValueCountFrequency (%)
240.435.2942
 
0.3%
25.457.9642
 
0.3%
202.838.6691
 
0.2%
244.101.3051
 
0.2%
252.061.2291
 
0.2%
245.471.6881
 
0.2%
237.605.1691
 
0.2%
236.950.8381
 
0.2%
236.261.1951
 
0.2%
244.276.0261
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:50.172786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
240.435.2942
 
2.1%
25.457.9642
 
2.1%
202.838.6691
 
1.1%
244.101.3051
 
1.1%
252.061.2291
 
1.1%
245.471.6881
 
1.1%
237.605.1691
 
1.1%
236.950.8381
 
1.1%
236.261.1951
 
1.1%
244.276.0261
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.189
18.3%
2184
17.8%
195
9.2%
483
8.0%
375
 
7.3%
673
 
7.1%
573
 
7.1%
969
 
6.7%
065
 
6.3%
864
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number843
81.7%
Other Punctuation189
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2184
21.8%
195
11.3%
483
9.8%
375
8.9%
673
 
8.7%
573
 
8.7%
969
 
8.2%
065
 
7.7%
864
 
7.6%
762
 
7.4%
Other Punctuation
ValueCountFrequency (%)
.189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.189
18.3%
2184
17.8%
195
9.2%
483
8.0%
375
 
7.3%
673
 
7.1%
573
 
7.1%
969
 
6.7%
065
 
6.3%
864
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.189
18.3%
2184
17.8%
195
9.2%
483
8.0%
375
 
7.3%
673
 
7.1%
573
 
7.1%
969
 
6.7%
065
 
6.3%
864
 
6.2%

PIB_Transporte
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
648.893.077
 
2
677.710.328
 
2
546.694.889
 
1
596.227.794
 
1
683.658.628
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.86315789
Min length7

Characters and Unicode

Total characters1032
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row546.694.889
2nd row544.306.818
3rd row536.681.481
4th row526.589.163
5th row536.446.432

Common Values

ValueCountFrequency (%)
648.893.0772
 
0.3%
677.710.3282
 
0.3%
546.694.8891
 
0.2%
596.227.7941
 
0.2%
683.658.6281
 
0.2%
63.045.0621
 
0.2%
625.572.6821
 
0.2%
611.412.9321
 
0.2%
625.437.4441
 
0.2%
612.630.1111
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:50.396991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
648.893.0772
 
2.1%
677.710.3282
 
2.1%
546.694.8891
 
1.1%
596.227.7941
 
1.1%
683.658.6281
 
1.1%
63.045.0621
 
1.1%
625.572.6821
 
1.1%
611.412.9321
 
1.1%
625.437.4441
 
1.1%
612.630.1111
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.189
18.3%
6125
12.1%
5121
11.7%
485
8.2%
181
7.8%
879
7.7%
275
 
7.3%
775
 
7.3%
971
 
6.9%
370
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number843
81.7%
Other Punctuation189
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6125
14.8%
5121
14.4%
485
10.1%
181
9.6%
879
9.4%
275
8.9%
775
8.9%
971
8.4%
370
8.3%
061
7.2%
Other Punctuation
ValueCountFrequency (%)
.189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.189
18.3%
6125
12.1%
5121
11.7%
485
8.2%
181
7.8%
879
7.7%
275
 
7.3%
775
 
7.3%
971
 
6.9%
370
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.189
18.3%
6125
12.1%
5121
11.7%
485
8.2%
181
7.8%
879
7.7%
275
 
7.3%
775
 
7.3%
971
 
6.9%
370
 
6.8%

PIB_Comunicaciones
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
417.627.795
 
2
422.758.463
 
2
337.915.263
 
1
390.410.157
 
1
446.302.808
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.86315789
Min length9

Characters and Unicode

Total characters1032
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row337.915.263
2nd row348.801.015
3rd row355.711.814
4th row353.452.988
5th row353.028.667

Common Values

ValueCountFrequency (%)
417.627.7952
 
0.3%
422.758.4632
 
0.3%
337.915.2631
 
0.2%
390.410.1571
 
0.2%
446.302.8081
 
0.2%
422.285.5431
 
0.2%
413.869.2951
 
0.2%
421.381.2561
 
0.2%
421.209.8261
 
0.2%
419.067.1141
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:50.618332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
417.627.7952
 
2.1%
422.758.4632
 
2.1%
337.915.2631
 
1.1%
390.410.1571
 
1.1%
446.302.8081
 
1.1%
422.285.5431
 
1.1%
413.869.2951
 
1.1%
421.381.2561
 
1.1%
421.209.8261
 
1.1%
419.067.1141
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
3118
11.4%
4115
11.1%
586
8.3%
983
8.0%
181
7.8%
776
 
7.4%
875
 
7.3%
072
 
7.0%
271
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number842
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3118
14.0%
4115
13.7%
586
10.2%
983
9.9%
181
9.6%
776
9.0%
875
8.9%
072
8.6%
271
8.4%
665
7.7%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
3118
11.4%
4115
11.1%
586
8.3%
983
8.0%
181
7.8%
776
 
7.4%
875
 
7.3%
072
 
7.0%
271
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
3118
11.4%
4115
11.1%
586
8.3%
983
8.0%
181
7.8%
776
 
7.4%
875
 
7.3%
072
 
7.0%
271
 
6.9%

PIB_Servicios_financieros
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
686.888.472
 
2
721.694.151
 
2
5.691.837
 
1
66.938.294
 
1
679.700.528
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.85263158
Min length9

Characters and Unicode

Total characters1031
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row5.691.837
2nd row565.071.466
3rd row571.394.036
4th row573.271.479
5th row574.971.364

Common Values

ValueCountFrequency (%)
686.888.4722
 
0.3%
721.694.1512
 
0.3%
5.691.8371
 
0.2%
66.938.2941
 
0.2%
679.700.5281
 
0.2%
676.713.1541
 
0.2%
67.900.3731
 
0.2%
678.036.7991
 
0.2%
678.738.4681
 
0.2%
668.992.2771
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:51.099596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
686.888.4722
 
2.1%
721.694.1512
 
2.1%
5.691.8371
 
1.1%
66.938.2941
 
1.1%
679.700.5281
 
1.1%
676.713.1541
 
1.1%
67.900.3731
 
1.1%
678.036.7991
 
1.1%
678.738.4681
 
1.1%
668.992.2771
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
6133
12.9%
7110
10.7%
589
8.6%
383
8.1%
880
7.8%
979
7.7%
179
7.7%
268
 
6.6%
464
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6133
15.8%
7110
13.1%
589
10.6%
383
9.9%
880
9.5%
979
9.4%
179
9.4%
268
8.1%
464
7.6%
056
6.7%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
6133
12.9%
7110
10.7%
589
8.6%
383
8.1%
880
7.8%
979
7.7%
179
7.7%
268
 
6.6%
464
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
6133
12.9%
7110
10.7%
589
8.6%
383
8.1%
880
7.8%
979
7.7%
179
7.7%
268
 
6.6%
464
 
6.2%

PIB_Servicios_empresariales
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
120.587.594
 
2
125.273.783
 
2
11.393.886
 
1
112.452.457
 
1
121.951.695
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.92631579
Min length10

Characters and Unicode

Total characters1038
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row11.393.886
2nd row121.229.491
3rd row125.792.137
4th row125.565.876
5th row124.038.548

Common Values

ValueCountFrequency (%)
120.587.5942
 
0.3%
125.273.7832
 
0.3%
11.393.8861
 
0.2%
112.452.4571
 
0.2%
121.951.6951
 
0.2%
122.441.2251
 
0.2%
12.228.7331
 
0.2%
124.051.9981
 
0.2%
126.095.7811
 
0.2%
124.862.1091
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:51.314021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
120.587.5942
 
2.1%
125.273.7832
 
2.1%
11.393.8861
 
1.1%
112.452.4571
 
1.1%
121.951.6951
 
1.1%
122.441.2251
 
1.1%
12.228.7331
 
1.1%
124.051.9981
 
1.1%
126.095.7811
 
1.1%
124.862.1091
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
1195
18.8%
.190
18.3%
2110
10.6%
394
9.1%
969
 
6.6%
868
 
6.6%
568
 
6.6%
766
 
6.4%
463
 
6.1%
662
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number848
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1195
23.0%
2110
13.0%
394
11.1%
969
 
8.1%
868
 
8.0%
568
 
8.0%
766
 
7.8%
463
 
7.4%
662
 
7.3%
053
 
6.2%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1195
18.8%
.190
18.3%
2110
10.6%
394
9.1%
969
 
6.6%
868
 
6.6%
568
 
6.6%
766
 
6.4%
463
 
6.1%
662
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1195
18.8%
.190
18.3%
2110
10.6%
394
9.1%
969
 
6.6%
868
 
6.6%
568
 
6.6%
766
 
6.4%
463
 
6.1%
662
 
6.0%

PIB_Servicios_de_vivienda
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
945.484.562
 
2
967.408.358
 
2
793.471.519
 
1
926.675.063
 
1
956.877.977
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.84210526
Min length10

Characters and Unicode

Total characters1030
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row793.471.519
2nd row80.455.108
3rd row805.307.434
4th row811.366.828
5th row813.086.052

Common Values

ValueCountFrequency (%)
945.484.5622
 
0.3%
967.408.3582
 
0.3%
793.471.5191
 
0.2%
926.675.0631
 
0.2%
956.877.9771
 
0.2%
951.544.6381
 
0.2%
945.056.9791
 
0.2%
942.442.6251
 
0.2%
936.799.8661
 
0.2%
932.720.5911
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:51.522486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
945.484.5622
 
2.1%
967.408.3582
 
2.1%
793.471.5191
 
1.1%
926.675.0631
 
1.1%
956.877.9771
 
1.1%
951.544.6381
 
1.1%
945.056.9791
 
1.1%
942.442.6251
 
1.1%
936.799.8661
 
1.1%
932.720.5911
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
9124
12.0%
8122
11.8%
786
8.3%
585
8.3%
281
7.9%
677
7.5%
474
 
7.2%
169
 
6.7%
067
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number840
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9124
14.8%
8122
14.5%
786
10.2%
585
10.1%
281
9.6%
677
9.2%
474
8.8%
169
8.2%
067
8.0%
355
6.5%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1030
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
9124
12.0%
8122
11.8%
786
8.3%
585
8.3%
281
7.9%
677
7.5%
474
 
7.2%
169
 
6.7%
067
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
9124
12.0%
8122
11.8%
786
8.3%
585
8.3%
281
7.9%
677
7.5%
474
 
7.2%
169
 
6.7%
067
 
6.5%

PIB_Servicios_personales
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
17.263.425
 
2
183.739.178
 
2
140.103.913
 
1
817.476.045
 
1
175.191.477
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.91578947
Min length10

Characters and Unicode

Total characters1037
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row140.103.913
2nd row144.685.333
3rd row147.859.706
4th row146.903.407
5th row108.506.835

Common Values

ValueCountFrequency (%)
17.263.4252
 
0.3%
183.739.1782
 
0.3%
140.103.9131
 
0.2%
817.476.0451
 
0.2%
175.191.4771
 
0.2%
150.888.0351
 
0.2%
131.921.0931
 
0.2%
174.286.0271
 
0.2%
176.960.9211
 
0.2%
174.708.8021
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:51.726427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17.263.4252
 
2.1%
183.739.1782
 
2.1%
140.103.9131
 
1.1%
817.476.0451
 
1.1%
175.191.4771
 
1.1%
150.888.0351
 
1.1%
131.921.0931
 
1.1%
174.286.0271
 
1.1%
176.960.9211
 
1.1%
174.708.8021
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.3%
1141
13.6%
694
9.1%
793
9.0%
587
8.4%
886
8.3%
377
7.4%
471
 
6.8%
068
 
6.6%
267
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number847
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1141
16.6%
694
11.1%
793
11.0%
587
10.3%
886
10.2%
377
9.1%
471
8.4%
068
8.0%
267
7.9%
963
7.4%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.3%
1141
13.6%
694
9.1%
793
9.0%
587
8.4%
886
8.3%
377
7.4%
471
 
6.8%
068
 
6.6%
267
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.3%
1141
13.6%
694
9.1%
793
9.0%
587
8.4%
886
8.3%
377
7.4%
471
 
6.8%
068
 
6.6%
267
 
6.5%

PIB_Administracion_publica
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
585.841.147
 
2
602.606.484
 
2
512.994.015
 
1
573.039.158
 
1
593.634.385
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.89473684
Min length9

Characters and Unicode

Total characters1035
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row512.994.015
2nd row511.803.995
3rd row512.469.359
4th row514.736.611
5th row515.375.539

Common Values

ValueCountFrequency (%)
585.841.1472
 
0.3%
602.606.4842
 
0.3%
512.994.0151
 
0.2%
573.039.1581
 
0.2%
593.634.3851
 
0.2%
592.310.7811
 
0.2%
584.276.9891
 
0.2%
584.471.3761
 
0.2%
5.806.4271
 
0.2%
577.423.0541
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:51.933880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
585.841.1472
 
2.1%
602.606.4842
 
2.1%
512.994.0151
 
1.1%
573.039.1581
 
1.1%
593.634.3851
 
1.1%
592.310.7811
 
1.1%
584.276.9891
 
1.1%
584.471.3761
 
1.1%
5.806.4271
 
1.1%
577.423.0541
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
5150
14.5%
695
9.2%
482
7.9%
181
7.8%
281
7.8%
874
 
7.1%
373
 
7.1%
972
 
7.0%
069
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number845
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5150
17.8%
695
11.2%
482
9.7%
181
9.6%
281
9.6%
874
8.8%
373
8.6%
972
8.5%
069
8.2%
768
8.0%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1035
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
5150
14.5%
695
9.2%
482
7.9%
181
7.8%
281
7.8%
874
 
7.1%
373
 
7.1%
972
 
7.0%
069
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
5150
14.5%
695
9.2%
482
7.9%
181
7.8%
281
7.8%
874
 
7.1%
373
 
7.1%
972
 
7.0%
069
 
6.7%

PIB_a_costo_de_factores
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
11.510.209
 
2
11955.81
 
2
108.482.797
 
1
107.541.772
 
1
119.147.799
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.73684211
Min length7

Characters and Unicode

Total characters1020
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row108.482.797
2nd row107.193.235
3rd row107.210.039
4th row105.389.875
5th row101.167.369

Common Values

ValueCountFrequency (%)
11.510.2092
 
0.3%
11955.812
 
0.3%
108.482.7971
 
0.2%
107.541.7721
 
0.2%
119.147.7991
 
0.2%
109.937.3571
 
0.2%
110.714.5451
 
0.2%
116.063.4241
 
0.2%
119.552.2331
 
0.2%
119.393.9171
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:52.153169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11.510.2092
 
2.1%
11955.812
 
2.1%
108.482.7971
 
1.1%
107.541.7721
 
1.1%
119.147.7991
 
1.1%
109.937.3571
 
1.1%
110.714.5451
 
1.1%
116.063.4241
 
1.1%
119.552.2331
 
1.1%
119.393.9171
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
1203
19.9%
.186
18.2%
089
8.7%
984
8.2%
579
 
7.7%
274
 
7.3%
771
 
7.0%
467
 
6.6%
862
 
6.1%
655
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number834
81.8%
Other Punctuation186
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1203
24.3%
089
10.7%
984
10.1%
579
 
9.5%
274
 
8.9%
771
 
8.5%
467
 
8.0%
862
 
7.4%
655
 
6.6%
350
 
6.0%
Other Punctuation
ValueCountFrequency (%)
.186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1203
19.9%
.186
18.2%
089
8.7%
984
8.2%
579
 
7.7%
274
 
7.3%
771
 
7.0%
467
 
6.6%
862
 
6.1%
655
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1203
19.9%
.186
18.2%
089
8.7%
984
8.2%
579
 
7.7%
274
 
7.3%
771
 
7.0%
467
 
6.6%
862
 
6.1%
655
 
5.4%

Impuesto_al_valor_agregado
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
104.105.171
 
2
107.485.012
 
2
896.688.023
 
1
949.689.852
 
1
105.894.111
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.91578947
Min length9

Characters and Unicode

Total characters1037
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row896.688.023
2nd row901.755.563
3rd row913.850.432
4th row883.855.814
5th row912.662.417

Common Values

ValueCountFrequency (%)
104.105.1712
 
0.3%
107.485.0122
 
0.3%
896.688.0231
 
0.2%
949.689.8521
 
0.2%
105.894.1111
 
0.2%
101.717.1821
 
0.2%
100.895.3111
 
0.2%
103.528.7251
 
0.2%
105.476.0531
 
0.2%
100.871.2051
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:52.371547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
104.105.1712
 
2.1%
107.485.0122
 
2.1%
896.688.0231
 
1.1%
949.689.8521
 
1.1%
105.894.1111
 
1.1%
101.717.1821
 
1.1%
100.895.3111
 
1.1%
103.528.7251
 
1.1%
105.476.0531
 
1.1%
100.871.2051
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.3%
1121
11.7%
9106
10.2%
089
8.6%
882
7.9%
281
7.8%
579
7.6%
477
7.4%
371
 
6.8%
771
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number847
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1121
14.3%
9106
12.5%
089
10.5%
882
9.7%
281
9.6%
579
9.3%
477
9.1%
371
8.4%
771
8.4%
670
8.3%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.3%
1121
11.7%
9106
10.2%
089
8.6%
882
7.9%
281
7.8%
579
7.6%
477
7.4%
371
 
6.8%
771
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.3%
1121
11.7%
9106
10.2%
089
8.6%
882
7.9%
281
7.8%
579
7.6%
477
7.4%
371
 
6.8%
771
 
6.8%

Derechos_de_Importacion
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
515.511.264
 
2
541.399.162
 
2
659.936.009
 
1
648.784.162
 
1
654.584.745
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.87368421
Min length9

Characters and Unicode

Total characters1033
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row659.936.009
2nd row59.890.634
3rd row436.004.645
4th row474.261.003
5th row546.557.712

Common Values

ValueCountFrequency (%)
515.511.2642
 
0.3%
541.399.1622
 
0.3%
659.936.0091
 
0.2%
648.784.1621
 
0.2%
654.584.7451
 
0.2%
543.564.6141
 
0.2%
672.817.5961
 
0.2%
707.103.4231
 
0.2%
711.033.5781
 
0.2%
594.958.8191
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:52.590930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
515.511.2642
 
2.1%
541.399.1622
 
2.1%
659.936.0091
 
1.1%
648.784.1621
 
1.1%
654.584.7451
 
1.1%
543.564.6141
 
1.1%
672.817.5961
 
1.1%
707.103.4231
 
1.1%
711.033.5781
 
1.1%
594.958.8191
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.4%
6117
11.3%
5106
10.3%
4103
10.0%
382
7.9%
879
7.6%
779
7.6%
273
 
7.1%
070
 
6.8%
170
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number843
81.6%
Other Punctuation190
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6117
13.9%
5106
12.6%
4103
12.2%
382
9.7%
879
9.4%
779
9.4%
273
8.7%
070
8.3%
170
8.3%
964
7.6%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.4%
6117
11.3%
5106
10.3%
4103
10.0%
382
7.9%
879
7.6%
779
7.6%
273
 
7.1%
070
 
6.8%
170
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.4%
6117
11.3%
5106
10.3%
4103
10.0%
382
7.9%
879
7.6%
779
7.6%
273
 
7.1%
070
 
6.8%
170
 
6.8%

PIB
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
12.603.131
 
2
130.851.849
 
2
118.109.613
 
1
117.685.853
 
1
130.391.916
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.91578947
Min length10

Characters and Unicode

Total characters1037
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row118.109.613
2nd row116.809.697
3rd row116.784.548
4th row114.702.694
5th row11.084.055

Common Values

ValueCountFrequency (%)
12.603.1312
 
0.3%
130.851.8492
 
0.3%
118.109.6131
 
0.2%
117.685.8531
 
0.2%
130.391.9161
 
0.2%
120.652.3431
 
0.2%
121.473.0371
 
0.2%
127.120.7481
 
0.2%
130.809.5271
 
0.2%
130.081.7411
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:52.805752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12.603.1312
 
2.1%
130.851.8492
 
2.1%
118.109.6131
 
1.1%
117.685.8531
 
1.1%
130.391.9161
 
1.1%
120.652.3431
 
1.1%
121.473.0371
 
1.1%
127.120.7481
 
1.1%
130.809.5271
 
1.1%
130.081.7411
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
1210
20.3%
.190
18.3%
299
9.5%
384
 
8.1%
879
 
7.6%
073
 
7.0%
770
 
6.8%
962
 
6.0%
458
 
5.6%
656
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number847
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1210
24.8%
299
11.7%
384
 
9.9%
879
 
9.3%
073
 
8.6%
770
 
8.3%
962
 
7.3%
458
 
6.8%
656
 
6.6%
556
 
6.6%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1210
20.3%
.190
18.3%
299
9.5%
384
 
8.1%
879
 
7.6%
073
 
7.0%
770
 
6.8%
962
 
6.0%
458
 
5.6%
656
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1210
20.3%
.190
18.3%
299
9.5%
384
 
8.1%
879
 
7.6%
073
 
7.0%
770
 
6.8%
962
 
6.0%
458
 
5.6%
656
 
5.4%

Precio_de_la_gasolina_en_EEUU_dolaresm3
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct581
Distinct (%)97.8%
Missing20
Missing (%)3.3%
Memory size4.9 KiB
28.4
 
6
132.97
 
2
120.92
 
2
245.26
 
2
53.532.204
 
2
Other values (576)
580 

Length

Max length10
Median length6
Mean length7.397306397
Min length2

Characters and Unicode

Total characters4394
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique572 ?
Unique (%)96.3%

Sample

1st row7.638.022
2nd row71.088.294
3rd row7.166.425
4th row70.787.106
5th row75.233.592

Common Values

ValueCountFrequency (%)
28.46
 
1.0%
132.972
 
0.3%
120.922
 
0.3%
245.262
 
0.3%
53.532.2042
 
0.3%
134.292
 
0.3%
20.602.3162
 
0.3%
43.529.5922
 
0.3%
105.632
 
0.3%
158.11
 
0.2%
Other values (571)571
93.0%
(Missing)20
 
3.3%

Length

2021-07-28T19:40:53.010541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28.46
 
1.0%
120.922
 
0.3%
53.532.2042
 
0.3%
132.972
 
0.3%
105.632
 
0.3%
245.262
 
0.3%
134.292
 
0.3%
20.602.3162
 
0.3%
43.529.5922
 
0.3%
158.11
 
0.2%
Other values (571)571
96.1%

Most occurring characters

ValueCountFrequency (%)
.834
19.0%
1499
11.4%
2489
11.1%
6377
8.6%
4358
8.1%
3345
7.9%
5329
 
7.5%
8314
 
7.1%
9300
 
6.8%
7275
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3560
81.0%
Other Punctuation834
 
19.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1499
14.0%
2489
13.7%
6377
10.6%
4358
10.1%
3345
9.7%
5329
9.2%
8314
8.8%
9300
8.4%
7275
7.7%
0274
7.7%
Other Punctuation
ValueCountFrequency (%)
.834
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.834
19.0%
1499
11.4%
2489
11.1%
6377
8.6%
4358
8.1%
3345
7.9%
5329
 
7.5%
8314
 
7.1%
9300
 
6.8%
7275
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.834
19.0%
1499
11.4%
2489
11.1%
6377
8.6%
4358
8.1%
3345
7.9%
5329
 
7.5%
8314
 
7.1%
9300
 
6.8%
7275
 
6.3%

Precio_de_la_onza_troy_de_oro_dolaresoz
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct607
Distinct (%)99.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean578.7065742
Minimum34.94
Maximum1969.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2021-07-28T19:40:53.112271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum34.94
5-th percentile63.51
Q1292.54
median385.04
Q3828.8
95-th percentile1565.588
Maximum1969.78
Range1934.84
Interquartile range (IQR)536.26

Descriptive statistics

Standard deviation468.0186464
Coefficient of variation (CV)0.8087322095
Kurtosis0.0363178328
Mean578.7065742
Median Absolute Deviation (MAD)128.78
Skewness1.118536147
Sum354747.13
Variance219041.4534
MonotonicityNot monotonic
2021-07-28T19:40:53.231951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412.842
 
0.3%
384.532
 
0.3%
37.442
 
0.3%
1200.362
 
0.3%
1503.52
 
0.3%
387.72
 
0.3%
385.581
 
0.2%
361.721
 
0.2%
366.721
 
0.2%
367.681
 
0.2%
Other values (597)597
97.2%
ValueCountFrequency (%)
34.941
0.2%
34.991
0.2%
35.091
0.2%
35.321
0.2%
35.381
0.2%
35.441
0.2%
35.621
0.2%
35.951
0.2%
36.191
0.2%
37.442
0.3%
ValueCountFrequency (%)
1969.781
0.2%
1923.351
0.2%
1900.361
0.2%
1866.351
0.2%
1846.371
0.2%
17721
0.2%
1763.931
0.2%
1745.781
0.2%
1744.361
0.2%
1741.631
0.2%

Precio_de_la_onza_troy_de_plata_dolaresoz
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct508
Distinct (%)82.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean54.20212268
Minimum1.32
Maximum431.028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2021-07-28T19:40:53.353625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.32
5-th percentile1.85
Q14.76
median6.08
Q335.08
95-th percentile237.2562
Maximum431.028
Range429.708
Interquartile range (IQR)30.32

Descriptive statistics

Standard deviation89.49385665
Coefficient of variation (CV)1.651113503
Kurtosis2.183778536
Mean54.20212268
Median Absolute Deviation (MAD)2.03
Skewness1.73778326
Sum33225.9012
Variance8009.150378
MonotonicityNot monotonic
2021-07-28T19:40:53.465353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.835
 
0.8%
5.374
 
0.7%
4.784
 
0.7%
4.054
 
0.7%
5.194
 
0.7%
5.554
 
0.7%
4.073
 
0.5%
5.663
 
0.5%
4.373
 
0.5%
4.763
 
0.5%
Other values (498)576
93.8%
ValueCountFrequency (%)
1.321
0.2%
1.341
0.2%
1.391
0.2%
1.421
0.2%
1.471
0.2%
1.51
0.2%
1.541
0.2%
1.572
0.3%
1.582
0.3%
1.591
0.2%
ValueCountFrequency (%)
431.0281
0.2%
403.4431
0.2%
381.2121
0.2%
380.4171
0.2%
368.6541
0.2%
359.1381
0.2%
357.5931
0.2%
342.1081
0.2%
338.0571
0.2%
332.5321
0.2%

Precio_del_cobre_refinado_BML_dolareslibra
Categorical

HIGH CARDINALITY
UNIFORM

Distinct474
Distinct (%)77.3%
Missing1
Missing (%)0.2%
Memory size4.9 KiB
01.01
 
7
0.79
 
6
1
 
6
0.75
 
5
1.1
 
5
Other values (469)
584 

Length

Max length11
Median length4
Mean length6.468189233
Min length1

Characters and Unicode

Total characters3965
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique387 ?
Unique (%)63.1%

Sample

1st row347.586.864
2nd row326.742.266
3rd row327.913
4th row317.701.624
5th row312.663.522

Common Values

ValueCountFrequency (%)
01.017
 
1.1%
0.796
 
1.0%
16
 
1.0%
0.755
 
0.8%
1.15
 
0.8%
01.075
 
0.8%
1.145
 
0.8%
0.654
 
0.7%
0.634
 
0.7%
1.114
 
0.7%
Other values (464)562
91.5%

Length

2021-07-28T19:40:53.693136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01.017
 
1.1%
0.796
 
1.0%
16
 
1.0%
0.755
 
0.8%
1.145
 
0.8%
01.075
 
0.8%
1.15
 
0.8%
0.634
 
0.7%
1.114
 
0.7%
0.654
 
0.7%
Other values (464)562
91.7%

Most occurring characters

ValueCountFrequency (%)
.686
17.3%
1433
10.9%
3352
8.9%
0342
8.6%
6339
8.5%
2333
8.4%
4321
8.1%
7317
8.0%
8292
7.4%
5275
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3279
82.7%
Other Punctuation686
 
17.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1433
13.2%
3352
10.7%
0342
10.4%
6339
10.3%
2333
10.2%
4321
9.8%
7317
9.7%
8292
8.9%
5275
8.4%
9275
8.4%
Other Punctuation
ValueCountFrequency (%)
.686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.686
17.3%
1433
10.9%
3352
8.9%
0342
8.6%
6339
8.5%
2333
8.4%
4321
8.1%
7317
8.0%
8292
7.4%
5275
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.686
17.3%
1433
10.9%
3352
8.9%
0342
8.6%
6339
8.5%
2333
8.4%
4321
8.1%
7317
8.0%
8292
7.4%
5275
6.9%

Precio_del_diesel_centavos_de_dolargalon
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct170
Distinct (%)98.8%
Missing442
Missing (%)72.0%
Infinite0
Infinite (%)0.0%
Mean215.3375581
Minimum79.49
Maximum384.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2021-07-28T19:40:53.787230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum79.49
5-th percentile112.3625
Q1162.4475
median200.34
Q3286.3925
95-th percentile319.569
Maximum384.38
Range304.89
Interquartile range (IQR)123.945

Descriptive statistics

Standard deviation68.62522427
Coefficient of variation (CV)0.3186867394
Kurtosis-0.8549968379
Mean215.3375581
Median Absolute Deviation (MAD)53.425
Skewness0.2820047986
Sum37038.06
Variance4709.421406
MonotonicityNot monotonic
2021-07-28T19:40:53.908960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.742
 
0.3%
211.372
 
0.3%
293.581
 
0.2%
244.651
 
0.2%
210.61
 
0.2%
206.571
 
0.2%
204.21
 
0.2%
209.351
 
0.2%
213.261
 
0.2%
225.061
 
0.2%
Other values (160)160
 
26.1%
(Missing)442
72.0%
ValueCountFrequency (%)
79.491
0.2%
84.161
0.2%
95.451
0.2%
99.371
0.2%
108.521
0.2%
108.761
0.2%
108.841
0.2%
111.151
0.2%
111.841
0.2%
112.791
0.2%
ValueCountFrequency (%)
384.381
0.2%
381.21
0.2%
373.111
0.2%
334.011
0.2%
326.871
0.2%
324.061
0.2%
323.041
0.2%
321.641
0.2%
320.351
0.2%
318.931
0.2%

Precio_del_gas_natural_dolaresmillon_de_unidades_termicas_britanicas
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct246
Distinct (%)97.2%
Missing361
Missing (%)58.8%
Infinite0
Infinite (%)0.0%
Mean5.055426482
Minimum1.6128
Maximum136.338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2021-07-28T19:40:54.021609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.6128
5-th percentile2.00118
Q12.8638
median3.8851
Q35.93
95-th percentile9.0564
Maximum136.338
Range134.7252
Interquartile range (IQR)3.0662

Descriptive statistics

Standard deviation8.580500301
Coefficient of variation (CV)1.697285151
Kurtosis219.6805116
Mean5.055426482
Median Absolute Deviation (MAD)1.1851
Skewness14.33673728
Sum1279.0229
Variance73.62498542
MonotonicityNot monotonic
2021-07-28T19:40:54.145849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.363
 
0.5%
2.96772
 
0.3%
3.292
 
0.3%
2.22622
 
0.3%
3.192
 
0.3%
3.82
 
0.3%
2.651
 
0.2%
2.571
 
0.2%
2.771
 
0.2%
3.81171
 
0.2%
Other values (236)236
38.4%
(Missing)361
58.8%
ValueCountFrequency (%)
1.61281
0.2%
1.70441
0.2%
1.73521
0.2%
1.73861
0.2%
1.75251
0.2%
1.79271
0.2%
1.90331
0.2%
1.91581
0.2%
1.91861
0.2%
1.92141
0.2%
ValueCountFrequency (%)
136.3381
0.2%
13.0491
0.2%
12.68241
0.2%
12.53151
0.2%
11.26761
0.2%
11.08361
0.2%
10.711
0.2%
10.621
0.2%
10.30051
0.2%
10.181
0.2%

Precio_del_petroleo_Brent_dolaresbarril
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct248
Distinct (%)98.0%
Missing361
Missing (%)58.8%
Infinite0
Infinite (%)0.0%
Mean63.67320158
Minimum18.6
Maximum133.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2021-07-28T19:40:54.263534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18.6
5-th percentile25.086
Q139.93
median59.37
Q379.27
95-th percentile113.532
Maximum133.9
Range115.3
Interquartile range (IQR)39.34

Descriptive statistics

Standard deviation29.70482433
Coefficient of variation (CV)0.4665200366
Kurtosis-0.8485016692
Mean63.67320158
Median Absolute Deviation (MAD)19.72
Skewness0.450295005
Sum16109.32
Variance882.3765885
MonotonicityNot monotonic
2021-07-28T19:40:54.385209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.132
 
0.3%
25.642
 
0.3%
59.252
 
0.3%
27.552
 
0.3%
62.332
 
0.3%
25.651
 
0.2%
23.691
 
0.2%
20.291
 
0.2%
19.491
 
0.2%
24.131
 
0.2%
Other values (238)238
38.8%
(Missing)361
58.8%
ValueCountFrequency (%)
18.61
0.2%
18.941
0.2%
19.491
0.2%
20.291
0.2%
20.481
0.2%
22.541
0.2%
23.341
0.2%
23.691
0.2%
24.131
0.2%
24.181
0.2%
ValueCountFrequency (%)
133.91
0.2%
133.051
0.2%
124.931
0.2%
123.941
0.2%
123.041
0.2%
120.461
0.2%
119.71
0.2%
116.461
0.2%
116.451
0.2%
114.461
0.2%

Precio_del_kerosene_dolaresm3
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct250
Distinct (%)98.8%
Missing361
Missing (%)58.8%
Memory size4.9 KiB
5.319.667
 
2
43.991.942
 
2
44.221.796
 
2
16.385.684
 
1
17.976.168
 
1
Other values (245)
245 

Length

Max length10
Median length10
Mean length9.743083004
Min length7

Characters and Unicode

Total characters2465
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique247 ?
Unique (%)97.6%

Sample

1st row76.520.246
2nd row71.791.066
3rd row71.318.148
4th row72.065.834
5th row7.488.749

Common Values

ValueCountFrequency (%)
5.319.6672
 
0.3%
43.991.9422
 
0.3%
44.221.7962
 
0.3%
16.385.6841
 
0.2%
17.976.1681
 
0.2%
1.895.6351
 
0.2%
18.359.2581
 
0.2%
15.947.1121
 
0.2%
13.846.7221
 
0.2%
13.220.5681
 
0.2%
Other values (240)240
39.1%
(Missing)361
58.8%

Length

2021-07-28T19:40:54.611129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5.319.6672
 
0.8%
43.991.9422
 
0.8%
44.221.7962
 
0.8%
16.385.6841
 
0.4%
19.574.5781
 
0.4%
17.976.1681
 
0.4%
1.895.6351
 
0.4%
18.359.2581
 
0.4%
15.947.1121
 
0.4%
13.846.7221
 
0.4%
Other values (240)240
94.9%

Most occurring characters

ValueCountFrequency (%)
.503
20.4%
2237
9.6%
6230
9.3%
4216
8.8%
3215
8.7%
8215
8.7%
7204
8.3%
5187
 
7.6%
1185
 
7.5%
9149
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1962
79.6%
Other Punctuation503
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2237
12.1%
6230
11.7%
4216
11.0%
3215
11.0%
8215
11.0%
7204
10.4%
5187
9.5%
1185
9.4%
9149
7.6%
0124
6.3%
Other Punctuation
ValueCountFrequency (%)
.503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2465
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.503
20.4%
2237
9.6%
6230
9.3%
4216
8.8%
3215
8.7%
8215
8.7%
7204
8.3%
5187
 
7.6%
1185
 
7.5%
9149
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.503
20.4%
2237
9.6%
6230
9.3%
4216
8.8%
3215
8.7%
8215
8.7%
7204
8.3%
5187
 
7.6%
1185
 
7.5%
9149
 
6.0%

Precio_del_petroleo_WTI_dolaresbarril
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct334
Distinct (%)73.7%
Missing161
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean43.06613687
Minimum11.3
Maximum133.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2021-07-28T19:40:54.704864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum11.3
5-th percentile15
Q120.1
median30.39
Q359.8
95-th percentile100.374
Maximum133.93
Range122.63
Interquartile range (IQR)39.7

Descriptive statistics

Standard deviation28.01443686
Coefficient of variation (CV)0.6504980223
Kurtosis-0.07512315246
Mean43.06613687
Median Absolute Deviation (MAD)12.99
Skewness0.9883169206
Sum19508.96
Variance784.8086724
MonotonicityNot monotonic
2021-07-28T19:40:54.822550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.96
 
1.0%
21.36
 
1.0%
186
 
1.0%
18.45
 
0.8%
19.75
 
0.8%
27.34
 
0.7%
19.64
 
0.7%
20.34
 
0.7%
20.14
 
0.7%
17.94
 
0.7%
Other values (324)405
66.0%
(Missing)161
 
26.2%
ValueCountFrequency (%)
11.31
0.2%
11.61
0.2%
121
0.2%
12.52
0.3%
12.91
0.2%
131
0.2%
13.41
0.2%
13.61
0.2%
13.71
0.2%
13.81
0.2%
ValueCountFrequency (%)
133.931
0.2%
133.381
0.2%
125.371
0.2%
116.641
0.2%
112.621
0.2%
109.961
0.2%
106.551
0.2%
106.251
0.2%
106.151
0.2%
105.471
0.2%

Precio_del_propano_centavos_de_dolargalon_DTN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct155
Distinct (%)98.7%
Missing457
Missing (%)74.4%
Infinite0
Infinite (%)0.0%
Mean88.52882166
Minimum29.233
Maximum187.875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2021-07-28T19:40:54.929264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum29.233
5-th percentile40.2276
Q154.888
median88.043
Q3110.738
95-th percentile152.7566
Maximum187.875
Range158.642
Interquartile range (IQR)55.85

Descriptive statistics

Standard deviation36.77245409
Coefficient of variation (CV)0.4153726821
Kurtosis-0.6012803362
Mean88.52882166
Median Absolute Deviation (MAD)30.043
Skewness0.4733577569
Sum13899.025
Variance1352.21338
MonotonicityNot monotonic
2021-07-28T19:40:55.036010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.3752
 
0.3%
97.4522
 
0.3%
89.1711
 
0.2%
128.51
 
0.2%
90.441
 
0.2%
94.8631
 
0.2%
100.9351
 
0.2%
107.3681
 
0.2%
119.0121
 
0.2%
131.8681
 
0.2%
Other values (145)145
 
23.6%
(Missing)457
74.4%
ValueCountFrequency (%)
29.2331
0.2%
32.561
0.2%
33.7371
0.2%
37.4291
0.2%
37.5131
0.2%
38.8241
0.2%
38.9661
0.2%
39.6381
0.2%
40.3752
0.3%
41.1821
0.2%
ValueCountFrequency (%)
187.8751
0.2%
180.8161
0.2%
169.7831
0.2%
164.1971
0.2%
158.651
0.2%
155.781
0.2%
153.8971
0.2%
152.8071
0.2%
152.7441
0.2%
152.0951
0.2%

Tipo_de_cambio_del_dolar_observado_diario
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct460
Distinct (%)99.6%
Missing152
Missing (%)24.8%
Memory size4.9 KiB
656.250.909
 
2
713.703.333
 
2
408.531.818
 
1
422.41
 
1
420.03
 
1
Other values (455)
455 

Length

Max length11
Median length11
Mean length9.898268398
Min length5

Characters and Unicode

Total characters4573
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique458 ?
Unique (%)99.1%

Sample

1st row472.484
2nd row472.137.273
3rd row479.582.857
4th row502.886
5th row504.962.273

Common Values

ValueCountFrequency (%)
656.250.9092
 
0.3%
713.703.3332
 
0.3%
408.531.8181
 
0.2%
422.411
 
0.2%
420.031
 
0.2%
415.552.6091
 
0.2%
411.844.4441
 
0.2%
411.100.4761
 
0.2%
410.723.0431
 
0.2%
409.846.8421
 
0.2%
Other values (450)450
73.3%
(Missing)152
 
24.8%

Length

2021-07-28T19:40:55.271349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
656.250.9092
 
0.4%
713.703.3332
 
0.4%
472.4841
 
0.2%
408.984.2111
 
0.2%
420.031
 
0.2%
415.552.6091
 
0.2%
411.844.4441
 
0.2%
411.100.4761
 
0.2%
410.723.0431
 
0.2%
409.846.8421
 
0.2%
Other values (450)450
97.4%

Most occurring characters

ValueCountFrequency (%)
.842
18.4%
4476
10.4%
5467
10.2%
6381
8.3%
2377
8.2%
1366
8.0%
7358
7.8%
3357
7.8%
8340
7.4%
9317
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3731
81.6%
Other Punctuation842
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4476
12.8%
5467
12.5%
6381
10.2%
2377
10.1%
1366
9.8%
7358
9.6%
3357
9.6%
8340
9.1%
9317
8.5%
0292
7.8%
Other Punctuation
ValueCountFrequency (%)
.842
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.842
18.4%
4476
10.4%
5467
10.2%
6381
8.3%
2377
8.2%
1366
8.0%
7358
7.8%
3357
7.8%
8340
7.4%
9317
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.842
18.4%
4476
10.4%
5467
10.2%
6381
8.3%
2377
8.2%
1366
8.0%
7358
7.8%
3357
7.8%
8340
7.4%
9317
 
6.9%

Ocupados
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct128
Distinct (%)98.5%
Missing484
Missing (%)78.8%
Memory size4.9 KiB
870.719.435
 
2
892.804.918
 
2
714.238.787
 
1
906.337.374
 
1
894.242.452
 
1
Other values (123)
123 

Length

Max length11
Median length11
Mean length10.86923077
Min length9

Characters and Unicode

Total characters1413
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)96.9%

Sample

1st row799.068.585
2nd row801.906.655
3rd row801.826.939
4th row800.424.589
5th row798.681.519

Common Values

ValueCountFrequency (%)
870.719.4352
 
0.3%
892.804.9182
 
0.3%
714.238.7871
 
0.2%
906.337.3741
 
0.2%
894.242.4521
 
0.2%
823.593.0791
 
0.2%
745.052.2561
 
0.2%
799.068.5851
 
0.2%
911.818.1111
 
0.2%
719.220.9791
 
0.2%
Other values (118)118
 
19.2%
(Missing)484
78.8%

Length

2021-07-28T19:40:55.490259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
870.719.4352
 
1.5%
892.804.9182
 
1.5%
908.713.2381
 
0.8%
718.190.2891
 
0.8%
719.877.7391
 
0.8%
715.621.1581
 
0.8%
76.676.5941
 
0.8%
736.505.5251
 
0.8%
719.220.9791
 
0.8%
707.319.2521
 
0.8%
Other values (118)118
90.8%

Most occurring characters

ValueCountFrequency (%)
.260
18.4%
8178
12.6%
7143
10.1%
9135
9.6%
2110
7.8%
5106
7.5%
6105
7.4%
1105
7.4%
097
 
6.9%
388
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1153
81.6%
Other Punctuation260
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8178
15.4%
7143
12.4%
9135
11.7%
2110
9.5%
5106
9.2%
6105
9.1%
1105
9.1%
097
8.4%
388
7.6%
486
7.5%
Other Punctuation
ValueCountFrequency (%)
.260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.260
18.4%
8178
12.6%
7143
10.1%
9135
9.6%
2110
7.8%
5106
7.5%
6105
7.4%
1105
7.4%
097
 
6.9%
388
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.260
18.4%
8178
12.6%
7143
10.1%
9135
9.6%
2110
7.8%
5106
7.5%
6105
7.4%
1105
7.4%
097
 
6.9%
388
 
6.2%

Ocupacion_en_Agricultura_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
641.968.676
 
2
640.428.548
 
2
798.092.771
 
1
789.470.825
 
1
649.560.005
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.93617021
Min length10

Characters and Unicode

Total characters1028
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row798.092.771
2nd row748.761.346
3rd row689.928.412
4th row643.035.387
5th row625.955.595

Common Values

ValueCountFrequency (%)
641.968.6762
 
0.3%
640.428.5482
 
0.3%
798.092.7711
 
0.2%
789.470.8251
 
0.2%
649.560.0051
 
0.2%
644.131.7841
 
0.2%
632.553.5691
 
0.2%
65.260.1281
 
0.2%
699.308.6291
 
0.2%
743.012.0461
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:55.709973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
641.968.6762
 
2.1%
640.428.5482
 
2.1%
798.092.7711
 
1.1%
765.046.8421
 
1.1%
644.131.7841
 
1.1%
632.553.5691
 
1.1%
65.260.1281
 
1.1%
699.308.6291
 
1.1%
743.012.0461
 
1.1%
768.560.5241
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.3%
6138
13.4%
7101
9.8%
488
8.6%
882
8.0%
379
7.7%
577
7.5%
275
 
7.3%
970
 
6.8%
169
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number840
81.7%
Other Punctuation188
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6138
16.4%
7101
12.0%
488
10.5%
882
9.8%
379
9.4%
577
9.2%
275
8.9%
970
8.3%
169
8.2%
061
7.3%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1028
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.3%
6138
13.4%
7101
9.8%
488
8.6%
882
8.0%
379
7.7%
577
7.5%
275
 
7.3%
970
 
6.8%
169
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.3%
6138
13.4%
7101
9.8%
488
8.6%
882
8.0%
379
7.7%
577
7.5%
275
 
7.3%
970
 
6.8%
169
 
6.7%

Ocupacion_en_Explotacion_de_minas_y_canteras_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
216.799.913
 
2
234.861.489
 
2
219.223.881
 
1
210.662.242
 
1
208.597.411
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.90526316
Min length9

Characters and Unicode

Total characters1036
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row258.428.756
2nd row25.564.008
3rd row2.630.406
4th row271.231.704
5th row268.516.519

Common Values

ValueCountFrequency (%)
216.799.9132
 
0.3%
234.861.4892
 
0.3%
219.223.8811
 
0.2%
210.662.2421
 
0.2%
208.597.4111
 
0.2%
211.418.8961
 
0.2%
220.244.5961
 
0.2%
210.363.7151
 
0.2%
217.509.3031
 
0.2%
213.356.0191
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:55.933969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
216.799.9132
 
2.1%
234.861.4892
 
2.1%
258.428.7561
 
1.1%
218.783.7881
 
1.1%
208.597.4111
 
1.1%
211.418.8961
 
1.1%
220.244.5961
 
1.1%
210.363.7151
 
1.1%
217.509.3031
 
1.1%
213.356.0191
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.190
18.3%
2162
15.6%
1100
9.7%
390
8.7%
483
8.0%
982
7.9%
675
 
7.2%
569
 
6.7%
069
 
6.7%
858
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number846
81.7%
Other Punctuation190
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2162
19.1%
1100
11.8%
390
10.6%
483
9.8%
982
9.7%
675
8.9%
569
8.2%
069
8.2%
858
 
6.9%
758
 
6.9%
Other Punctuation
ValueCountFrequency (%)
.190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.190
18.3%
2162
15.6%
1100
9.7%
390
8.7%
483
8.0%
982
7.9%
675
 
7.2%
569
 
6.7%
069
 
6.7%
858
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.190
18.3%
2162
15.6%
1100
9.7%
390
8.7%
483
8.0%
982
7.9%
675
 
7.2%
569
 
6.7%
069
 
6.7%
858
 
5.6%

Ocupacion_en_Industrias_manufactureras_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
918.492.209
 
2
856.467.155
 
2
901.504.814
 
1
937.816.508
 
1
911.755.074
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.86170213
Min length10

Characters and Unicode

Total characters1021
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row901.504.814
2nd row872.935.008
3rd row885.031.604
4th row893.552.645
5th row90.967.219

Common Values

ValueCountFrequency (%)
918.492.2092
 
0.3%
856.467.1552
 
0.3%
901.504.8141
 
0.2%
937.816.5081
 
0.2%
911.755.0741
 
0.2%
90.527.6061
 
0.2%
907.772.3061
 
0.2%
899.894.4571
 
0.2%
891.840.9591
 
0.2%
906.352.6391
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:56.157372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
918.492.2092
 
2.1%
856.467.1552
 
2.1%
901.504.8141
 
1.1%
951.646.1871
 
1.1%
90.527.6061
 
1.1%
907.772.3061
 
1.1%
899.894.4571
 
1.1%
891.840.9591
 
1.1%
906.352.6391
 
1.1%
940.976.5751
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.4%
9134
13.1%
898
9.6%
682
8.0%
580
7.8%
178
7.6%
077
7.5%
776
7.4%
473
 
7.1%
273
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number833
81.6%
Other Punctuation188
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9134
16.1%
898
11.8%
682
9.8%
580
9.6%
178
9.4%
077
9.2%
776
9.1%
473
8.8%
273
8.8%
362
7.4%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.4%
9134
13.1%
898
9.6%
682
8.0%
580
7.8%
178
7.6%
077
7.5%
776
7.4%
473
 
7.1%
273
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.4%
9134
13.1%
898
9.6%
682
8.0%
580
7.8%
178
7.6%
077
7.5%
776
7.4%
473
 
7.1%
273
 
7.1%

Ocupacion_en_Suministro_de_electricidad_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
423.859.029
 
2
435.285.983
 
2
360.108.494
 
1
414.720.274
 
1
508.584.427
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.85263158
Min length1

Characters and Unicode

Total characters1031
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row360.108.494
2nd row35.161.487
3rd row370.478.187
4th row393.545.812
5th row422.440.362

Common Values

ValueCountFrequency (%)
423.859.0292
 
0.3%
435.285.9832
 
0.3%
360.108.4941
 
0.2%
414.720.2741
 
0.2%
508.584.4271
 
0.2%
490.698.1411
 
0.2%
432.113.8681
 
0.2%
410.312.0731
 
0.2%
41.248.0281
 
0.2%
408.245.3971
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:56.391147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
423.859.0292
 
2.1%
435.285.9832
 
2.1%
360.108.4941
 
1.1%
414.720.2741
 
1.1%
508.584.4271
 
1.1%
490.698.1411
 
1.1%
432.113.8681
 
1.1%
410.312.0731
 
1.1%
41.248.0281
 
1.1%
408.245.3971
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.188
18.2%
4139
13.5%
288
8.5%
386
8.3%
886
8.3%
784
8.1%
183
8.1%
579
7.7%
968
 
6.6%
066
 
6.4%
Other values (2)64
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number842
81.7%
Other Punctuation188
 
18.2%
Lowercase Letter1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4139
16.5%
288
10.5%
386
10.2%
886
10.2%
784
10.0%
183
9.9%
579
9.4%
968
8.1%
066
7.8%
663
7.5%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%
Lowercase Letter
ValueCountFrequency (%)
a1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1030
99.9%
Latin1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.3%
4139
13.5%
288
8.5%
386
8.3%
886
8.3%
784
8.2%
183
8.1%
579
7.7%
968
 
6.6%
066
 
6.4%
Latin
ValueCountFrequency (%)
a1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.2%
4139
13.5%
288
8.5%
386
8.3%
886
8.3%
784
8.1%
183
8.1%
579
7.7%
968
 
6.6%
066
 
6.4%
Other values (2)64
 
6.2%

Ocupacion_en_Actividades_de_servicios_administrativos_y_de_apoyo_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
216.848.578
 
2
241.649.229
 
2
18.951.548
 
1
222.760.281
 
1
223.181.376
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.87234043
Min length9

Characters and Unicode

Total characters1022
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row18.951.548
2nd row189.184.206
3rd row183.186.272
4th row174.068.814
5th row163.224.557

Common Values

ValueCountFrequency (%)
216.848.5782
 
0.3%
241.649.2292
 
0.3%
18.951.5481
 
0.2%
222.760.2811
 
0.2%
223.181.3761
 
0.2%
207.298.0521
 
0.2%
217.437.5551
 
0.2%
230.000.0011
 
0.2%
226.011.8971
 
0.2%
239.173.6851
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:56.629527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
216.848.5782
 
2.1%
241.649.2292
 
2.1%
18.951.5481
 
1.1%
220.045.9551
 
1.1%
207.298.0521
 
1.1%
217.437.5551
 
1.1%
230.000.0011
 
1.1%
226.011.8971
 
1.1%
239.173.6851
 
1.1%
227.591.2981
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.4%
2170
16.6%
197
9.5%
580
7.8%
374
 
7.2%
973
 
7.1%
072
 
7.0%
671
 
6.9%
867
 
6.6%
466
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number834
81.6%
Other Punctuation188
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2170
20.4%
197
11.6%
580
9.6%
374
8.9%
973
8.8%
072
8.6%
671
8.5%
867
 
8.0%
466
 
7.9%
764
 
7.7%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1022
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.4%
2170
16.6%
197
9.5%
580
7.8%
374
 
7.2%
973
 
7.1%
072
 
7.0%
671
 
6.9%
867
 
6.6%
466
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.4%
2170
16.6%
197
9.5%
580
7.8%
374
 
7.2%
973
 
7.1%
072
 
7.0%
671
 
6.9%
867
 
6.6%
466
 
6.5%

Ocupacion_en_Actividades_profesionales_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
293.308
 
2
297.915.329
 
2
229.653.619
 
1
275.846.858
 
1
284.954.466
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.84042553
Min length7

Characters and Unicode

Total characters1019
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row229.653.619
2nd row245.403.126
3rd row259.778.478
4th row256.673.756
5th row249.403.461

Common Values

ValueCountFrequency (%)
293.3082
 
0.3%
297.915.3292
 
0.3%
229.653.6191
 
0.2%
275.846.8581
 
0.2%
284.954.4661
 
0.2%
286.084.6281
 
0.2%
302.466.7631
 
0.2%
328.535.5791
 
0.2%
321.696.4381
 
0.2%
310.377.9071
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:56.872876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
293.3082
 
2.1%
297.915.3292
 
2.1%
229.653.6191
 
1.1%
285.480.6641
 
1.1%
286.084.6281
 
1.1%
302.466.7631
 
1.1%
328.535.5791
 
1.1%
321.696.4381
 
1.1%
310.377.9071
 
1.1%
283.740.0411
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.186
18.3%
2150
14.7%
490
8.8%
585
8.3%
983
8.1%
779
7.8%
878
7.7%
675
7.4%
374
 
7.3%
063
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number833
81.7%
Other Punctuation186
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2150
18.0%
490
10.8%
585
10.2%
983
10.0%
779
9.5%
878
9.4%
675
9.0%
374
8.9%
063
7.6%
156
 
6.7%
Other Punctuation
ValueCountFrequency (%)
.186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1019
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.186
18.3%
2150
14.7%
490
8.8%
585
8.3%
983
8.1%
779
7.8%
878
7.7%
675
7.4%
374
 
7.3%
063
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.186
18.3%
2150
14.7%
490
8.8%
585
8.3%
983
8.1%
779
7.8%
878
7.7%
675
7.4%
374
 
7.3%
063
 
6.2%

Ocupacion_en_Actividades_inmobiliarias_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
827.010.405
 
2
77.525.851
 
2
547.033.124
 
1
892.048.934
 
1
910.100.949
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.88297872
Min length9

Characters and Unicode

Total characters1023
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row547.033.124
2nd row517.938.307
3rd row523.538.906
4th row541.516.672
5th row685.736.504

Common Values

ValueCountFrequency (%)
827.010.4052
 
0.3%
77.525.8512
 
0.3%
547.033.1241
 
0.2%
892.048.9341
 
0.2%
910.100.9491
 
0.2%
87.052.1661
 
0.2%
82.286.6831
 
0.2%
896.024.0611
 
0.2%
868.198.5411
 
0.2%
918.908.4521
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:57.508333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
827.010.4052
 
2.1%
77.525.8512
 
2.1%
547.033.1241
 
1.1%
83.818.9521
 
1.1%
87.052.1661
 
1.1%
82.286.6831
 
1.1%
896.024.0611
 
1.1%
868.198.5411
 
1.1%
918.908.4521
 
1.1%
867.298.8031
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.4%
8110
10.8%
6110
10.8%
790
8.8%
190
8.8%
278
7.6%
576
7.4%
973
 
7.1%
470
 
6.8%
370
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number835
81.6%
Other Punctuation188
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8110
13.2%
6110
13.2%
790
10.8%
190
10.8%
278
9.3%
576
9.1%
973
8.7%
470
8.4%
370
8.4%
068
8.1%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1023
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.4%
8110
10.8%
6110
10.8%
790
8.8%
190
8.8%
278
7.6%
576
7.4%
973
 
7.1%
470
 
6.8%
370
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.4%
8110
10.8%
6110
10.8%
790
8.8%
190
8.8%
278
7.6%
576
7.4%
973
 
7.1%
470
 
6.8%
370
 
6.8%

Ocupacion_en_Actividades_financieras_y_de_seguros_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
163.289.707
 
2
164.612.138
 
2
19.153.327
 
1
167.091.696
 
1
168.963.053
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.89361702
Min length9

Characters and Unicode

Total characters1024
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row19.153.327
2nd row200.181.753
3rd row204.803.422
4th row19.556.226
5th row190.390.057

Common Values

ValueCountFrequency (%)
163.289.7072
 
0.3%
164.612.1382
 
0.3%
19.153.3271
 
0.2%
167.091.6961
 
0.2%
168.963.0531
 
0.2%
170.005.1611
 
0.2%
16.732.8451
 
0.2%
166.967.5491
 
0.2%
168.185.9761
 
0.2%
170.808.7671
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:57.735462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
163.289.7072
 
2.1%
164.612.1382
 
2.1%
19.153.3271
 
1.1%
165.629.3941
 
1.1%
170.005.1611
 
1.1%
16.732.8451
 
1.1%
166.967.5491
 
1.1%
168.185.9761
 
1.1%
170.808.7671
 
1.1%
170.455.2571
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.4%
1163
15.9%
698
9.6%
990
8.8%
886
8.4%
779
7.7%
568
 
6.6%
267
 
6.5%
465
 
6.3%
362
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number836
81.6%
Other Punctuation188
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1163
19.5%
698
11.7%
990
10.8%
886
10.3%
779
9.4%
568
8.1%
267
8.0%
465
 
7.8%
362
 
7.4%
058
 
6.9%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1024
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.4%
1163
15.9%
698
9.6%
990
8.8%
886
8.4%
779
7.7%
568
 
6.6%
267
 
6.5%
465
 
6.3%
362
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.4%
1163
15.9%
698
9.6%
990
8.8%
886
8.4%
779
7.7%
568
 
6.6%
267
 
6.5%
465
 
6.3%
362
 
6.1%

Ocupacion_en_Informacion_y_comunicaciones_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
166.817.299
 
2
162.758.745
 
2
155.736.824
 
1
173.156.226
 
1
174.390.267
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.85263158
Min length1

Characters and Unicode

Total characters1031
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row155.736.824
2nd row163.543.996
3rd row164.546.013
4th row167.439.052
5th row158.353.388

Common Values

ValueCountFrequency (%)
166.817.2992
 
0.3%
162.758.7452
 
0.3%
155.736.8241
 
0.2%
173.156.2261
 
0.2%
174.390.2671
 
0.2%
174.438.8871
 
0.2%
178.360.9551
 
0.2%
174.645.6431
 
0.2%
174.752.8741
 
0.2%
175.001.2151
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:40:57.945410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
166.817.2992
 
2.1%
162.758.7452
 
2.1%
155.736.8241
 
1.1%
173.156.2261
 
1.1%
174.390.2671
 
1.1%
174.438.8871
 
1.1%
178.360.9551
 
1.1%
174.645.6431
 
1.1%
174.752.8741
 
1.1%
175.001.2151
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.188
18.2%
1161
15.6%
491
8.8%
590
8.7%
689
8.6%
783
8.1%
880
7.8%
375
 
7.3%
959
 
5.7%
258
 
5.6%
Other values (2)57
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number842
81.7%
Other Punctuation188
 
18.2%
Lowercase Letter1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1161
19.1%
491
10.8%
590
10.7%
689
10.6%
783
9.9%
880
9.5%
375
8.9%
959
 
7.0%
258
 
6.9%
056
 
6.7%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%
Lowercase Letter
ValueCountFrequency (%)
a1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1030
99.9%
Latin1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.3%
1161
15.6%
491
8.8%
590
8.7%
689
8.6%
783
8.1%
880
7.8%
375
 
7.3%
959
 
5.7%
258
 
5.6%
Latin
ValueCountFrequency (%)
a1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.2%
1161
15.6%
491
8.8%
590
8.7%
689
8.6%
783
8.1%
880
7.8%
375
 
7.3%
959
 
5.7%
258
 
5.6%
Other values (2)57
 
5.5%

Ocupacion_en_Transporte_y_almacenamiento_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
589.477.133
 
2
560.102.952
 
2
498.854.717
 
1
579.225.627
 
1
581.970.142
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.92553191
Min length10

Characters and Unicode

Total characters1027
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row498.854.717
2nd row50.361.222
3rd row502.470.761
4th row514.471.177
5th row509.933.563

Common Values

ValueCountFrequency (%)
589.477.1332
 
0.3%
560.102.9522
 
0.3%
498.854.7171
 
0.2%
579.225.6271
 
0.2%
581.970.1421
 
0.2%
583.087.7061
 
0.2%
58.300.4831
 
0.2%
598.462.6451
 
0.2%
598.383.1251
 
0.2%
60.084.2361
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:58.152870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
589.477.1332
 
2.1%
560.102.9522
 
2.1%
498.854.7171
 
1.1%
576.018.9631
 
1.1%
583.087.7061
 
1.1%
58.300.4831
 
1.1%
598.462.6451
 
1.1%
598.383.1251
 
1.1%
60.084.2361
 
1.1%
570.977.2771
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.3%
5163
15.9%
386
8.4%
685
8.3%
482
8.0%
074
 
7.2%
172
 
7.0%
272
 
7.0%
970
 
6.8%
869
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number839
81.7%
Other Punctuation188
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5163
19.4%
386
10.3%
685
10.1%
482
9.8%
074
8.8%
172
8.6%
272
8.6%
970
8.3%
869
8.2%
766
7.9%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.3%
5163
15.9%
386
8.4%
685
8.3%
482
8.0%
074
 
7.2%
172
 
7.0%
272
 
7.0%
970
 
6.8%
869
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.3%
5163
15.9%
386
8.4%
685
8.3%
482
8.0%
074
 
7.2%
172
 
7.0%
272
 
7.0%
970
 
6.8%
869
 
6.7%

Ocupacion_en_Actividades_de_alojamiento_y_de_servicio_de_comidas_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
452.211.964
 
2
452.651.215
 
2
341.857.582
 
1
434.921.372
 
1
44.545.486
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.90425532
Min length9

Characters and Unicode

Total characters1025
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row341.857.582
2nd row31.736.525
3rd row30.683.457
4th row30.862.596
5th row326.787.184

Common Values

ValueCountFrequency (%)
452.211.9642
 
0.3%
452.651.2152
 
0.3%
341.857.5821
 
0.2%
434.921.3721
 
0.2%
44.545.4861
 
0.2%
439.417.7721
 
0.2%
451.829.7591
 
0.2%
442.782.7621
 
0.2%
43.162.4671
 
0.2%
430.708.2261
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:58.373266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
452.211.9642
 
2.1%
452.651.2152
 
2.1%
341.857.5821
 
1.1%
411.954.8781
 
1.1%
439.417.7721
 
1.1%
451.829.7591
 
1.1%
442.782.7621
 
1.1%
43.162.4671
 
1.1%
430.708.2261
 
1.1%
427.593.4561
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.3%
4128
12.5%
3125
12.2%
291
8.9%
985
8.3%
876
7.4%
776
7.4%
174
 
7.2%
573
 
7.1%
663
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number837
81.7%
Other Punctuation188
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4128
15.3%
3125
14.9%
291
10.9%
985
10.2%
876
9.1%
776
9.1%
174
8.8%
573
8.7%
663
7.5%
046
 
5.5%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1025
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.3%
4128
12.5%
3125
12.2%
291
8.9%
985
8.3%
876
7.4%
776
7.4%
174
 
7.2%
573
 
7.1%
663
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.3%
4128
12.5%
3125
12.2%
291
8.9%
985
8.3%
876
7.4%
776
7.4%
174
 
7.2%
573
 
7.1%
663
 
6.1%

Ocupacion_en_Construccion_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
742.884.096
 
2
759.290.823
 
2
682.423.108
 
1
727.274.907
 
1
746.324.937
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.93617021
Min length10

Characters and Unicode

Total characters1028
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row682.423.108
2nd row714.330.143
3rd row703.343.896
4th row698.814.201
5th row685.679.331

Common Values

ValueCountFrequency (%)
742.884.0962
 
0.3%
759.290.8232
 
0.3%
682.423.1081
 
0.2%
727.274.9071
 
0.2%
746.324.9371
 
0.2%
742.466.3921
 
0.2%
750.460.3351
 
0.2%
736.853.7421
 
0.2%
726.067.6751
 
0.2%
71.862.0271
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:58.591058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
742.884.0962
 
2.1%
759.290.8232
 
2.1%
682.423.1081
 
1.1%
726.262.6781
 
1.1%
742.466.3921
 
1.1%
750.460.3351
 
1.1%
736.853.7421
 
1.1%
726.067.6751
 
1.1%
71.862.0271
 
1.1%
731.325.2341
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.3%
7123
12.0%
395
9.2%
693
9.0%
285
8.3%
881
7.9%
580
7.8%
479
7.7%
070
 
6.8%
968
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number840
81.7%
Other Punctuation188
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7123
14.6%
395
11.3%
693
11.1%
285
10.1%
881
9.6%
580
9.5%
479
9.4%
070
8.3%
968
8.1%
166
7.9%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1028
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.3%
7123
12.0%
395
9.2%
693
9.0%
285
8.3%
881
7.9%
580
7.8%
479
7.7%
070
 
6.8%
968
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.3%
7123
12.0%
395
9.2%
693
9.0%
285
8.3%
881
7.9%
580
7.8%
479
7.7%
070
 
6.8%
968
 
6.6%

Ocupacion_en_Comercio_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
164.123.096
 
2
172.267.313
 
2
151.916.628
 
1
165.359.717
 
1
167.513.809
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.86170213
Min length9

Characters and Unicode

Total characters1021
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row151.916.628
2nd row155.050.122
3rd row15.611.683
4th row156.308.276
5th row1.561.431

Common Values

ValueCountFrequency (%)
164.123.0962
 
0.3%
172.267.3132
 
0.3%
151.916.6281
 
0.2%
165.359.7171
 
0.2%
167.513.8091
 
0.2%
166.464.6771
 
0.2%
161.971.0941
 
0.2%
16.391.2851
 
0.2%
164.961.3761
 
0.2%
165.998.1741
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:58.815458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
164.123.0962
 
2.1%
172.267.3132
 
2.1%
151.916.6281
 
1.1%
164.844.1351
 
1.1%
166.464.6771
 
1.1%
161.971.0941
 
1.1%
16.391.2851
 
1.1%
164.961.3761
 
1.1%
165.998.1741
 
1.1%
164.676.0541
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.4%
1161
15.8%
6123
12.0%
595
9.3%
777
7.5%
477
7.5%
267
 
6.6%
962
 
6.1%
361
 
6.0%
056
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number833
81.6%
Other Punctuation188
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1161
19.3%
6123
14.8%
595
11.4%
777
9.2%
477
9.2%
267
8.0%
962
 
7.4%
361
 
7.3%
056
 
6.7%
854
 
6.5%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.4%
1161
15.8%
6123
12.0%
595
9.3%
777
7.5%
477
7.5%
267
 
6.6%
962
 
6.1%
361
 
6.0%
056
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.4%
1161
15.8%
6123
12.0%
595
9.3%
777
7.5%
477
7.5%
267
 
6.6%
962
 
6.1%
361
 
6.0%
056
 
5.5%

Ocupacion_en_Suministro_de_agua_evacuacion_de_aguas_residuales_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)94.9%
Missing516
Missing (%)84.0%
Memory size4.9 KiB
a
 
4
388.556.009
 
2
530.643.655
 
2
473.761.226
 
1
463.921.736
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.45918367
Min length1

Characters and Unicode

Total characters1025
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)91.8%

Sample

1st row349.444.258
2nd row357.165.171
3rd row387.134.632
4th row398.815.137
5th row397.905.417

Common Values

ValueCountFrequency (%)
a4
 
0.7%
388.556.0092
 
0.3%
530.643.6552
 
0.3%
473.761.2261
 
0.2%
463.921.7361
 
0.2%
434.576.0271
 
0.2%
439.632.7321
 
0.2%
479.933.7861
 
0.2%
499.989.5931
 
0.2%
452.125.9271
 
0.2%
Other values (83)83
 
13.5%
(Missing)516
84.0%

Length

2021-07-28T19:40:59.023619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a4
 
4.1%
388.556.0092
 
2.0%
530.643.6552
 
2.0%
473.761.2261
 
1.0%
463.921.7361
 
1.0%
434.576.0271
 
1.0%
439.632.7321
 
1.0%
479.933.7861
 
1.0%
499.989.5931
 
1.0%
452.125.9271
 
1.0%
Other values (83)83
84.7%

Most occurring characters

ValueCountFrequency (%)
.188
18.3%
4129
12.6%
5100
9.8%
391
8.9%
682
8.0%
978
7.6%
878
7.6%
177
7.5%
271
 
6.9%
769
 
6.7%
Other values (2)62
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number833
81.3%
Other Punctuation188
 
18.3%
Lowercase Letter4
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4129
15.5%
5100
12.0%
391
10.9%
682
9.8%
978
9.4%
878
9.4%
177
9.2%
271
8.5%
769
8.3%
058
7.0%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%
Lowercase Letter
ValueCountFrequency (%)
a4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1021
99.6%
Latin4
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.4%
4129
12.6%
5100
9.8%
391
8.9%
682
8.0%
978
7.6%
878
7.6%
177
7.5%
271
 
7.0%
769
 
6.8%
Latin
ValueCountFrequency (%)
a4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.3%
4129
12.6%
5100
9.8%
391
8.9%
682
8.0%
978
7.6%
878
7.6%
177
7.5%
271
 
6.9%
769
 
6.7%
Other values (2)62
 
6.0%

Ocupacion_en_Administracion_publica_y_defensa_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
470.714.131
 
2
532.961.264
 
2
445.435.266
 
1
477.529.617
 
1
474.563.055
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.88297872
Min length9

Characters and Unicode

Total characters1023
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row445.435.266
2nd row437.306.264
3rd row429.622.301
4th row429.793.264
5th row421.899.254

Common Values

ValueCountFrequency (%)
470.714.1312
 
0.3%
532.961.2642
 
0.3%
445.435.2661
 
0.2%
477.529.6171
 
0.2%
474.563.0551
 
0.2%
469.686.2411
 
0.2%
472.066.5241
 
0.2%
477.927.8531
 
0.2%
464.621.8611
 
0.2%
464.994.9311
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:59.238051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
470.714.1312
 
2.1%
532.961.2642
 
2.1%
445.435.2661
 
1.1%
475.807.7071
 
1.1%
469.686.2411
 
1.1%
472.066.5241
 
1.1%
477.927.8531
 
1.1%
464.621.8611
 
1.1%
464.994.9311
 
1.1%
465.511.6451
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.4%
4152
14.9%
795
9.3%
385
8.3%
183
8.1%
680
7.8%
279
7.7%
577
7.5%
964
 
6.3%
864
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number835
81.6%
Other Punctuation188
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4152
18.2%
795
11.4%
385
10.2%
183
9.9%
680
9.6%
279
9.5%
577
9.2%
964
7.7%
864
7.7%
056
 
6.7%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1023
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.4%
4152
14.9%
795
9.3%
385
8.3%
183
8.1%
680
7.8%
279
7.7%
577
7.5%
964
 
6.3%
864
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.4%
4152
14.9%
795
9.3%
385
8.3%
183
8.1%
680
7.8%
279
7.7%
577
7.5%
964
 
6.3%
864
 
6.3%

Ocupacion_en_Enseanza_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
788.049.995
 
2
819.048.173
 
2
587.837.412
 
1
722.388.056
 
1
795.391.784
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.81914894
Min length7

Characters and Unicode

Total characters1017
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row587.837.412
2nd row609.994.331
3rd row63.826.243
4th row648.435.359
5th row675.429.113

Common Values

ValueCountFrequency (%)
788.049.9952
 
0.3%
819.048.1732
 
0.3%
587.837.4121
 
0.2%
722.388.0561
 
0.2%
795.391.7841
 
0.2%
795.781.1471
 
0.2%
79.802.0021
 
0.2%
78.555.3331
 
0.2%
786.941.7771
 
0.2%
754.094.3731
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:59.461965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
788.049.9952
 
2.1%
819.048.1732
 
2.1%
587.837.4121
 
1.1%
757.444.3171
 
1.1%
795.781.1471
 
1.1%
79.802.0021
 
1.1%
78.555.3331
 
1.1%
786.941.7771
 
1.1%
754.094.3731
 
1.1%
723.745.0291
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.187
18.4%
7133
13.1%
689
8.8%
886
8.5%
386
8.5%
581
8.0%
979
7.8%
273
 
7.2%
471
 
7.0%
171
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number830
81.6%
Other Punctuation187
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7133
16.0%
689
10.7%
886
10.4%
386
10.4%
581
9.8%
979
9.5%
273
8.8%
471
8.6%
171
8.6%
061
7.3%
Other Punctuation
ValueCountFrequency (%)
.187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1017
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.187
18.4%
7133
13.1%
689
8.8%
886
8.5%
386
8.5%
581
8.0%
979
7.8%
273
 
7.2%
471
 
7.0%
171
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.187
18.4%
7133
13.1%
689
8.8%
886
8.5%
386
8.5%
581
8.0%
979
7.8%
273
 
7.2%
471
 
7.0%
171
 
7.0%

Ocupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
489.831.763
 
2
556.868.981
 
2
362.517.856
 
1
474.214.524
 
1
506.459.846
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.91489362
Min length10

Characters and Unicode

Total characters1026
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row362.517.856
2nd row36.143.856
3rd row364.909.451
4th row364.323.921
5th row367.557.412

Common Values

ValueCountFrequency (%)
489.831.7632
 
0.3%
556.868.9812
 
0.3%
362.517.8561
 
0.2%
474.214.5241
 
0.2%
506.459.8461
 
0.2%
497.017.1521
 
0.2%
498.126.9421
 
0.2%
49.499.6551
 
0.2%
497.101.3381
 
0.2%
478.731.2021
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:59.669583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
489.831.7632
 
2.1%
556.868.9812
 
2.1%
362.517.8561
 
1.1%
46.791.6751
 
1.1%
497.017.1521
 
1.1%
498.126.9421
 
1.1%
49.499.6551
 
1.1%
497.101.3381
 
1.1%
478.731.2021
 
1.1%
474.845.3551
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.3%
4141
13.7%
599
9.6%
688
8.6%
188
8.6%
386
8.4%
286
8.4%
975
 
7.3%
768
 
6.6%
864
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number838
81.7%
Other Punctuation188
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4141
16.8%
599
11.8%
688
10.5%
188
10.5%
386
10.3%
286
10.3%
975
8.9%
768
8.1%
864
7.6%
043
 
5.1%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1026
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.3%
4141
13.7%
599
9.6%
688
8.6%
188
8.6%
386
8.4%
286
8.4%
975
 
7.3%
768
 
6.6%
864
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.3%
4141
13.7%
599
9.6%
688
8.6%
188
8.6%
386
8.4%
286
8.4%
975
 
7.3%
768
 
6.6%
864
 
6.2%

Ocupacion_en_Actividades_artisticas_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
113.514.038
 
2
128.238.355
 
2
82.567.788
 
1
104.829.994
 
1
108.947.877
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.92553191
Min length10

Characters and Unicode

Total characters1027
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row82.567.788
2nd row897.101.326
3rd row928.991.289
4th row922.830.791
5th row961.558.663

Common Values

ValueCountFrequency (%)
113.514.0382
 
0.3%
128.238.3552
 
0.3%
82.567.7881
 
0.2%
104.829.9941
 
0.2%
108.947.8771
 
0.2%
11.100.9611
 
0.2%
109.305.5911
 
0.2%
113.304.1271
 
0.2%
106.837.7261
 
0.2%
104.996.9651
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:40:59.874071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
113.514.0382
 
2.1%
128.238.3552
 
2.1%
82.567.7881
 
1.1%
110.497.4121
 
1.1%
11.100.9611
 
1.1%
109.305.5911
 
1.1%
113.304.1271
 
1.1%
106.837.7261
 
1.1%
104.996.9651
 
1.1%
104.908.7481
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.3%
1162
15.8%
090
8.8%
881
7.9%
980
7.8%
674
 
7.2%
474
 
7.2%
571
 
6.9%
270
 
6.8%
369
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number839
81.7%
Other Punctuation188
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1162
19.3%
090
10.7%
881
9.7%
980
9.5%
674
8.8%
474
8.8%
571
8.5%
270
8.3%
369
8.2%
768
8.1%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.3%
1162
15.8%
090
8.8%
881
7.9%
980
7.8%
674
 
7.2%
474
 
7.2%
571
 
6.9%
270
 
6.8%
369
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.3%
1162
15.8%
090
8.8%
881
7.9%
980
7.8%
674
 
7.2%
474
 
7.2%
571
 
6.9%
270
 
6.8%
369
 
6.7%

Ocupacion_en_Otras_actividades_de_servicios_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct93
Distinct (%)97.9%
Missing519
Missing (%)84.5%
Memory size4.9 KiB
27.638.401
 
2
268.749.784
 
2
201.875.908
 
1
281.417.768
 
1
28.045.038
 
1
Other values (88)
88 

Length

Max length11
Median length11
Mean length10.78947368
Min length1

Characters and Unicode

Total characters1025
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)95.8%

Sample

1st row201.875.908
2nd row202.053.864
3rd row200.806.454
4th row201.437.383
5th row187.860.463

Common Values

ValueCountFrequency (%)
27.638.4012
 
0.3%
268.749.7842
 
0.3%
201.875.9081
 
0.2%
281.417.7681
 
0.2%
28.045.0381
 
0.2%
28.083.5751
 
0.2%
274.780.4051
 
0.2%
268.493.0731
 
0.2%
271.011.3481
 
0.2%
276.573.3941
 
0.2%
Other values (83)83
 
13.5%
(Missing)519
84.5%

Length

2021-07-28T19:41:00.078492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27.638.4012
 
2.1%
268.749.7842
 
2.1%
201.875.9081
 
1.1%
281.417.7681
 
1.1%
28.045.0381
 
1.1%
28.083.5751
 
1.1%
274.780.4051
 
1.1%
268.493.0731
 
1.1%
271.011.3481
 
1.1%
276.573.3941
 
1.1%
Other values (83)83
87.4%

Most occurring characters

ValueCountFrequency (%)
.188
18.3%
2154
15.0%
892
9.0%
789
8.7%
481
7.9%
180
7.8%
379
7.7%
969
 
6.7%
067
 
6.5%
565
 
6.3%
Other values (2)61
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number836
81.6%
Other Punctuation188
 
18.3%
Lowercase Letter1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2154
18.4%
892
11.0%
789
10.6%
481
9.7%
180
9.6%
379
9.4%
969
8.3%
067
8.0%
565
7.8%
660
 
7.2%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%
Lowercase Letter
ValueCountFrequency (%)
a1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1024
99.9%
Latin1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.4%
2154
15.0%
892
9.0%
789
8.7%
481
7.9%
180
7.8%
379
7.7%
969
 
6.7%
067
 
6.5%
565
 
6.3%
Latin
ValueCountFrequency (%)
a1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.3%
2154
15.0%
892
9.0%
789
8.7%
481
7.9%
180
7.8%
379
7.7%
969
 
6.7%
067
 
6.5%
565
 
6.3%
Other values (2)61
 
6.0%

Ocupacion_en_Actividades_de_los_hogares_como_empleadores_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct92
Distinct (%)97.9%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
359.984.869
 
2
353.145.094
 
2
416.124.956
 
1
365.109.655
 
1
348.802.659
 
1
Other values (87)
87 

Length

Max length11
Median length11
Mean length10.90425532
Min length9

Characters and Unicode

Total characters1025
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)95.7%

Sample

1st row416.124.956
2nd row433.420.434
3rd row438.571.154
4th row447.361.174
5th row436.743.687

Common Values

ValueCountFrequency (%)
359.984.8692
 
0.3%
353.145.0942
 
0.3%
416.124.9561
 
0.2%
365.109.6551
 
0.2%
348.802.6591
 
0.2%
348.065.7011
 
0.2%
354.013.5341
 
0.2%
35.369.4381
 
0.2%
354.783.5941
 
0.2%
353.506.8391
 
0.2%
Other values (82)82
 
13.4%
(Missing)520
84.7%

Length

2021-07-28T19:41:00.292918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
359.984.8692
 
2.1%
353.145.0942
 
2.1%
416.124.9561
 
1.1%
380.179.7691
 
1.1%
348.065.7011
 
1.1%
354.013.5341
 
1.1%
35.369.4381
 
1.1%
354.783.5941
 
1.1%
353.506.8391
 
1.1%
3.661.4111
 
1.1%
Other values (82)82
87.2%

Most occurring characters

ValueCountFrequency (%)
.188
18.3%
3163
15.9%
5102
10.0%
490
8.8%
177
7.5%
275
 
7.3%
870
 
6.8%
770
 
6.8%
669
 
6.7%
968
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number837
81.7%
Other Punctuation188
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3163
19.5%
5102
12.2%
490
10.8%
177
9.2%
275
9.0%
870
8.4%
770
8.4%
669
8.2%
968
8.1%
053
 
6.3%
Other Punctuation
ValueCountFrequency (%)
.188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1025
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.188
18.3%
3163
15.9%
5102
10.0%
490
8.8%
177
7.5%
275
 
7.3%
870
 
6.8%
770
 
6.8%
669
 
6.7%
968
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.188
18.3%
3163
15.9%
5102
10.0%
490
8.8%
177
7.5%
275
 
7.3%
870
 
6.8%
770
 
6.8%
669
 
6.7%
968
 
6.6%

Ocupacion_en_Actividades_de_organizaciones_y_organos_extraterritoriales_INE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct89
Distinct (%)94.7%
Missing520
Missing (%)84.7%
Memory size4.9 KiB
0
 
4
144.446.086
 
2
150.795.138
 
2
179.325.544
 
1
271.656.492
 
1
Other values (84)
84 

Length

Max length11
Median length11
Mean length10.34042553
Min length1

Characters and Unicode

Total characters972
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)91.5%

Sample

1st row190.085.519
2nd row101.278.125
3rd row95.097.066
4th row66.623.637
5th row121.432.645

Common Values

ValueCountFrequency (%)
04
 
0.7%
144.446.0862
 
0.3%
150.795.1382
 
0.3%
179.325.5441
 
0.2%
271.656.4921
 
0.2%
22.914.2691
 
0.2%
202.318.1831
 
0.2%
231.795.6561
 
0.2%
359.181.9851
 
0.2%
490.828.5331
 
0.2%
Other values (79)79
 
12.9%
(Missing)520
84.7%

Length

2021-07-28T19:41:00.509437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
04
 
4.3%
144.446.0862
 
2.1%
150.795.1382
 
2.1%
179.325.5441
 
1.1%
271.656.4921
 
1.1%
22.914.2691
 
1.1%
202.318.1831
 
1.1%
231.795.6561
 
1.1%
359.181.9851
 
1.1%
490.828.5331
 
1.1%
Other values (79)79
84.0%

Most occurring characters

ValueCountFrequency (%)
.179
18.4%
1106
10.9%
292
9.5%
392
9.5%
581
8.3%
681
8.3%
480
8.2%
068
 
7.0%
867
 
6.9%
966
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number793
81.6%
Other Punctuation179
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1106
13.4%
292
11.6%
392
11.6%
581
10.2%
681
10.2%
480
10.1%
068
8.6%
867
8.4%
966
8.3%
760
7.6%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.179
18.4%
1106
10.9%
292
9.5%
392
9.5%
581
8.3%
681
8.3%
480
8.2%
068
 
7.0%
867
 
6.9%
966
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.179
18.4%
1106
10.9%
292
9.5%
392
9.5%
581
8.3%
681
8.3%
480
8.2%
068
 
7.0%
867
 
6.9%
966
 
6.8%

No_sabe__No_responde_Miles_de_personas
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct10
Distinct (%)100.0%
Missing604
Missing (%)98.4%
Memory size4.9 KiB
348.150.992
576.118.269
586.175.509
273.067.521
611.221.631
Other values (5)

Length

Max length11
Median length11
Mean length10.9
Min length10

Characters and Unicode

Total characters109
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row348.150.992
2nd row576.118.269
3rd row586.175.509
4th row273.067.521
5th row611.221.631

Common Values

ValueCountFrequency (%)
348.150.9921
 
0.2%
576.118.2691
 
0.2%
586.175.5091
 
0.2%
273.067.5211
 
0.2%
611.221.6311
 
0.2%
979.885.4631
 
0.2%
210.013.1231
 
0.2%
250.863.1331
 
0.2%
292.671.1941
 
0.2%
26.169.5531
 
0.2%
(Missing)604
98.4%

Length

2021-07-28T19:41:00.673041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-28T19:41:00.725519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
348.150.9921
10.0%
576.118.2691
10.0%
586.175.5091
10.0%
273.067.5211
10.0%
611.221.6311
10.0%
979.885.4631
10.0%
210.013.1231
10.0%
250.863.1331
10.0%
292.671.1941
10.0%
26.169.5531
10.0%

Most occurring characters

ValueCountFrequency (%)
.20
18.3%
116
14.7%
212
11.0%
611
10.1%
310
9.2%
510
9.2%
99
8.3%
86
 
5.5%
06
 
5.5%
76
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number89
81.7%
Other Punctuation20
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116
18.0%
212
13.5%
611
12.4%
310
11.2%
510
11.2%
99
10.1%
86
 
6.7%
06
 
6.7%
76
 
6.7%
43
 
3.4%
Other Punctuation
ValueCountFrequency (%)
.20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common109
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.20
18.3%
116
14.7%
212
11.0%
611
10.1%
310
9.2%
510
9.2%
99
8.3%
86
 
5.5%
06
 
5.5%
76
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.20
18.3%
116
14.7%
212
11.0%
611
10.1%
310
9.2%
510
9.2%
99
8.3%
86
 
5.5%
06
 
5.5%
76
 
5.5%

Tipo_de_cambio_nominal_multilateral___TCM
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct311
Distinct (%)99.4%
Missing301
Missing (%)49.0%
Memory size4.9 KiB
106.596.667
 
2
102.822.273
 
2
93.621
 
1
108.346.818
 
1
116.714.286
 
1
Other values (306)
306 

Length

Max length11
Median length11
Mean length9.910543131
Min length5

Characters and Unicode

Total characters3102
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique309 ?
Unique (%)98.7%

Sample

1st row93.621
2nd row934.109.091
3rd row944.752.381
4th row985.085
5th row982.236.364

Common Values

ValueCountFrequency (%)
106.596.6672
 
0.3%
102.822.2732
 
0.3%
93.6211
 
0.2%
108.346.8181
 
0.2%
116.714.2861
 
0.2%
115.981
 
0.2%
116.606.6671
 
0.2%
114.507.7271
 
0.2%
113.665.2381
 
0.2%
111.0951
 
0.2%
Other values (301)301
49.0%
(Missing)301
49.0%

Length

2021-07-28T19:41:01.226363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
106.596.6672
 
0.6%
102.822.2732
 
0.6%
93.6211
 
0.3%
108.346.8181
 
0.3%
116.714.2861
 
0.3%
115.981
 
0.3%
116.606.6671
 
0.3%
114.507.7271
 
0.3%
113.665.2381
 
0.3%
111.0951
 
0.3%
Other values (301)301
96.2%

Most occurring characters

ValueCountFrequency (%)
.571
18.4%
1549
17.7%
0314
10.1%
5258
8.3%
2228
 
7.4%
9215
 
6.9%
3211
 
6.8%
4206
 
6.6%
7186
 
6.0%
6185
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2531
81.6%
Other Punctuation571
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1549
21.7%
0314
12.4%
5258
10.2%
2228
9.0%
9215
 
8.5%
3211
 
8.3%
4206
 
8.1%
7186
 
7.3%
6185
 
7.3%
8179
 
7.1%
Other Punctuation
ValueCountFrequency (%)
.571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.571
18.4%
1549
17.7%
0314
10.1%
5258
8.3%
2228
 
7.4%
9215
 
6.9%
3211
 
6.8%
4206
 
6.6%
7186
 
6.0%
6185
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.571
18.4%
1549
17.7%
0314
10.1%
5258
8.3%
2228
 
7.4%
9215
 
6.9%
3211
 
6.8%
4206
 
6.6%
7186
 
6.0%
6185
 
6.0%

Indice_de_tipo_de_cambio_real___TCR_promedio_1986_100
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct418
Distinct (%)99.5%
Missing194
Missing (%)31.6%
Memory size4.9 KiB
948.715.254
 
2
911.086.301
 
2
865.903.468
 
1
786.646.412
 
1
780.277.622
 
1
Other values (413)
413 

Length

Max length11
Median length11
Mean length10.87619048
Min length9

Characters and Unicode

Total characters4568
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)99.0%

Sample

1st row865.903.468
2nd row867.806.065
3rd row878.030.947
4th row913.180.348
5th row909.263.447

Common Values

ValueCountFrequency (%)
948.715.2542
 
0.3%
911.086.3012
 
0.3%
865.903.4681
 
0.2%
786.646.4121
 
0.2%
780.277.6221
 
0.2%
769.702.6411
 
0.2%
76.622.7081
 
0.2%
752.652.3131
 
0.2%
76.068.6981
 
0.2%
765.344.7051
 
0.2%
Other values (408)408
66.4%
(Missing)194
31.6%

Length

2021-07-28T19:41:01.443818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
948.715.2542
 
0.5%
911.086.3012
 
0.5%
865.903.4681
 
0.2%
784.551.2681
 
0.2%
769.702.6411
 
0.2%
76.622.7081
 
0.2%
752.652.3131
 
0.2%
76.068.6981
 
0.2%
765.344.7051
 
0.2%
77.758.9941
 
0.2%
Other values (408)408
97.1%

Most occurring characters

ValueCountFrequency (%)
.840
18.4%
1477
10.4%
9476
10.4%
8411
9.0%
0376
8.2%
7367
8.0%
2339
7.4%
3333
 
7.3%
6324
 
7.1%
4317
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3728
81.6%
Other Punctuation840
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1477
12.8%
9476
12.8%
8411
11.0%
0376
10.1%
7367
9.8%
2339
9.1%
3333
8.9%
6324
8.7%
4317
8.5%
5308
8.3%
Other Punctuation
ValueCountFrequency (%)
.840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.840
18.4%
1477
10.4%
9476
10.4%
8411
9.0%
0376
8.2%
7367
8.0%
2339
7.4%
3333
 
7.3%
6324
 
7.1%
4317
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.840
18.4%
1477
10.4%
9476
10.4%
8411
9.0%
0376
8.2%
7367
8.0%
2339
7.4%
3333
 
7.3%
6324
 
7.1%
4317
 
6.9%

Indice_de_produccion_industrial
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct142
Distinct (%)98.6%
Missing470
Missing (%)76.5%
Memory size4.9 KiB
104.034.103
 
2
102.691.109
 
2
102.761.705
 
1
877.709.354
 
1
862.848.016
 
1
Other values (137)
137 

Length

Max length11
Median length11
Mean length10.95138889
Min length10

Characters and Unicode

Total characters1577
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)97.2%

Sample

1st row102.761.705
2nd row968.087.179
3rd row978.471.874
4th row966.647.135
5th row100.100.749

Common Values

ValueCountFrequency (%)
104.034.1032
 
0.3%
102.691.1092
 
0.3%
102.761.7051
 
0.2%
877.709.3541
 
0.2%
862.848.0161
 
0.2%
796.396.3171
 
0.2%
898.551.0291
 
0.2%
853.936.0951
 
0.2%
863.880.6321
 
0.2%
884.201.9421
 
0.2%
Other values (132)132
 
21.5%
(Missing)470
76.5%

Length

2021-07-28T19:41:01.667254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
104.034.1032
 
1.4%
102.691.1092
 
1.4%
102.761.7051
 
0.7%
843.373.8041
 
0.7%
862.848.0161
 
0.7%
796.396.3171
 
0.7%
898.551.0291
 
0.7%
853.936.0951
 
0.7%
863.880.6321
 
0.7%
877.709.3541
 
0.7%
Other values (132)132
91.7%

Most occurring characters

ValueCountFrequency (%)
.288
18.3%
9179
11.4%
1175
11.1%
0146
9.3%
8125
7.9%
2119
7.5%
6115
 
7.3%
3113
 
7.2%
5108
 
6.8%
4107
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1289
81.7%
Other Punctuation288
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9179
13.9%
1175
13.6%
0146
11.3%
8125
9.7%
2119
9.2%
6115
8.9%
3113
8.8%
5108
8.4%
4107
8.3%
7102
7.9%
Other Punctuation
ValueCountFrequency (%)
.288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.288
18.3%
9179
11.4%
1175
11.1%
0146
9.3%
8125
7.9%
2119
7.5%
6115
 
7.3%
3113
 
7.2%
5108
 
6.8%
4107
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.288
18.3%
9179
11.4%
1175
11.1%
0146
9.3%
8125
7.9%
2119
7.5%
6115
 
7.3%
3113
 
7.2%
5108
 
6.8%
4107
 
6.8%

Indice_de_produccion_industrial__mineria
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct355
Distinct (%)95.4%
Missing242
Missing (%)39.4%
Memory size4.9 KiB
337.440.681
 
2
41.963.777
 
2
470.933.698
 
2
927.108.978
 
2
326.934.286
 
2
Other values (350)
362 

Length

Max length11
Median length11
Mean length10.89516129
Min length7

Characters and Unicode

Total characters4053
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique338 ?
Unique (%)90.9%

Sample

1st row98.915.705
2nd row914.276.663
3rd row966.913.278
4th row981.820.974
5th row990.198.382

Common Values

ValueCountFrequency (%)
337.440.6812
 
0.3%
41.963.7772
 
0.3%
470.933.6982
 
0.3%
927.108.9782
 
0.3%
326.934.2862
 
0.3%
102.619.9222
 
0.3%
913.185.9642
 
0.3%
780.507.8242
 
0.3%
983.228.2212
 
0.3%
959.869.0132
 
0.3%
Other values (345)352
57.3%
(Missing)242
39.4%

Length

2021-07-28T19:41:01.887632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
337.440.6812
 
0.5%
41.963.7772
 
0.5%
470.933.6982
 
0.5%
534.281.0782
 
0.5%
927.108.9782
 
0.5%
326.934.2862
 
0.5%
102.619.9222
 
0.5%
780.507.8242
 
0.5%
913.185.9642
 
0.5%
959.869.0132
 
0.5%
Other values (345)352
94.6%

Most occurring characters

ValueCountFrequency (%)
.743
18.3%
9425
10.5%
8373
9.2%
1350
8.6%
3342
8.4%
7333
8.2%
5311
7.7%
0304
7.5%
4297
 
7.3%
6291
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3310
81.7%
Other Punctuation743
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9425
12.8%
8373
11.3%
1350
10.6%
3342
10.3%
7333
10.1%
5311
9.4%
0304
9.2%
4297
9.0%
6291
8.8%
2284
8.6%
Other Punctuation
ValueCountFrequency (%)
.743
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4053
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.743
18.3%
9425
10.5%
8373
9.2%
1350
8.6%
3342
8.4%
7333
8.2%
5311
7.7%
0304
7.5%
4297
 
7.3%
6291
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.743
18.3%
9425
10.5%
8373
9.2%
1350
8.6%
3342
8.4%
7333
8.2%
5311
7.7%
0304
7.5%
4297
 
7.3%
6291
 
7.2%

Indice_de_produccion_industrial_electricidad__gas_y_agua
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct82
Distinct (%)97.6%
Missing530
Missing (%)86.3%
Memory size4.9 KiB
107.666.732
 
2
108.792.744
 
2
111.046.063
 
1
108.502.235
 
1
102.825.581
 
1
Other values (77)
77 

Length

Max length11
Median length11
Mean length10.86904762
Min length10

Characters and Unicode

Total characters913
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)95.2%

Sample

1st row103.655.736
2nd row97.391.498
3rd row104.907.399
4th row992.049.154
5th row101.106.965

Common Values

ValueCountFrequency (%)
107.666.7322
 
0.3%
108.792.7442
 
0.3%
111.046.0631
 
0.2%
108.502.2351
 
0.2%
102.825.5811
 
0.2%
103.484.3361
 
0.2%
100.329.6721
 
0.2%
112.435.6191
 
0.2%
111.951.0161
 
0.2%
105.816.9371
 
0.2%
Other values (72)72
 
11.7%
(Missing)530
86.3%

Length

2021-07-28T19:41:02.106586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
107.666.7322
 
2.4%
108.792.7442
 
2.4%
103.655.7361
 
1.2%
111.046.0631
 
1.2%
108.502.2351
 
1.2%
102.825.5811
 
1.2%
103.484.3361
 
1.2%
100.329.6721
 
1.2%
112.435.6191
 
1.2%
111.951.0161
 
1.2%
Other values (72)72
85.7%

Most occurring characters

ValueCountFrequency (%)
.168
18.4%
1145
15.9%
0100
11.0%
978
8.5%
376
8.3%
664
 
7.0%
563
 
6.9%
261
 
6.7%
459
 
6.5%
752
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number745
81.6%
Other Punctuation168
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1145
19.5%
0100
13.4%
978
10.5%
376
10.2%
664
8.6%
563
8.5%
261
8.2%
459
7.9%
752
 
7.0%
847
 
6.3%
Other Punctuation
ValueCountFrequency (%)
.168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.168
18.4%
1145
15.9%
0100
11.0%
978
8.5%
376
8.3%
664
 
7.0%
563
 
6.9%
261
 
6.7%
459
 
6.5%
752
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.168
18.4%
1145
15.9%
0100
11.0%
978
8.5%
376
8.3%
664
 
7.0%
563
 
6.9%
261
 
6.7%
459
 
6.5%
752
 
5.7%

Indice_de_produccion_industrial__manufacturera
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct340
Distinct (%)94.4%
Missing254
Missing (%)41.4%
Memory size4.9 KiB
68.979.954
 
3
689.318.173
 
3
578.603.662
 
2
749.489.103
 
2
847.965.195
 
2
Other values (335)
348 

Length

Max length11
Median length11
Mean length10.84722222
Min length7

Characters and Unicode

Total characters3905
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique322 ?
Unique (%)89.4%

Sample

1st row108.387.837
2nd row104.545.429
3rd row99.468.801
4th row94.969.678
5th row102.128.977

Common Values

ValueCountFrequency (%)
68.979.9543
 
0.5%
689.318.1733
 
0.5%
578.603.6622
 
0.3%
749.489.1032
 
0.3%
847.965.1952
 
0.3%
877.230.6022
 
0.3%
90.137.4532
 
0.3%
688.355.4382
 
0.3%
105.596.5352
 
0.3%
63.540.5022
 
0.3%
Other values (330)338
55.0%
(Missing)254
41.4%

Length

2021-07-28T19:41:02.341964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
68.979.9543
 
0.8%
689.318.1733
 
0.8%
63.540.5022
 
0.6%
578.603.6622
 
0.6%
749.489.1032
 
0.6%
847.965.1952
 
0.6%
877.230.6022
 
0.6%
90.137.4532
 
0.6%
688.355.4382
 
0.6%
105.596.5352
 
0.6%
Other values (330)338
93.9%

Most occurring characters

ValueCountFrequency (%)
.719
18.4%
9371
9.5%
1350
9.0%
6348
8.9%
7346
8.9%
8343
8.8%
0298
7.6%
5295
7.6%
3290
7.4%
4287
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3186
81.6%
Other Punctuation719
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9371
11.6%
1350
11.0%
6348
10.9%
7346
10.9%
8343
10.8%
0298
9.4%
5295
9.3%
3290
9.1%
4287
9.0%
2258
8.1%
Other Punctuation
ValueCountFrequency (%)
.719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.719
18.4%
9371
9.5%
1350
9.0%
6348
8.9%
7346
8.9%
8343
8.8%
0298
7.6%
5295
7.6%
3290
7.4%
4287
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.719
18.4%
9371
9.5%
1350
9.0%
6348
8.9%
7346
8.9%
8343
8.8%
0298
7.6%
5295
7.6%
3290
7.4%
4287
 
7.3%

Generacion_de_energia_electrica_CDEC_GWh
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct262
Distinct (%)99.2%
Missing350
Missing (%)57.0%
Memory size4.9 KiB
654.060.172
 
2
6598
 
2
580.480.672
 
1
321.872.877
 
1
349.346.911
 
1
Other values (257)
257 

Length

Max length11
Median length11
Mean length10.37121212
Min length4

Characters and Unicode

Total characters2738
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique260 ?
Unique (%)98.5%

Sample

1st row580.480.672
2nd row544.815.032
3rd row56.891.916
4th row56.594.867
5th row586.329.899

Common Values

ValueCountFrequency (%)
654.060.1722
 
0.3%
65982
 
0.3%
580.480.6721
 
0.2%
321.872.8771
 
0.2%
349.346.9111
 
0.2%
350.528.0381
 
0.2%
334.305.6351
 
0.2%
341.007.2871
 
0.2%
344.551.8571
 
0.2%
36.229.7381
 
0.2%
Other values (252)252
41.0%
(Missing)350
57.0%

Length

2021-07-28T19:41:02.573395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
654.060.1722
 
0.8%
65982
 
0.8%
321.872.8771
 
0.4%
321.558.1391
 
0.4%
349.346.9111
 
0.4%
350.528.0381
 
0.4%
334.305.6351
 
0.4%
341.007.2871
 
0.4%
344.551.8571
 
0.4%
348.904.7731
 
0.4%
Other values (252)252
95.5%

Most occurring characters

ValueCountFrequency (%)
.498
18.2%
4284
10.4%
5264
9.6%
6250
9.1%
3249
9.1%
7221
8.1%
1206
7.5%
9206
7.5%
8202
7.4%
2201
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
81.8%
Other Punctuation498
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4284
12.7%
5264
11.8%
6250
11.2%
3249
11.1%
7221
9.9%
1206
9.2%
9206
9.2%
8202
9.0%
2201
9.0%
0157
7.0%
Other Punctuation
ValueCountFrequency (%)
.498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2738
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.498
18.2%
4284
10.4%
5264
9.6%
6250
9.1%
3249
9.1%
7221
8.1%
1206
7.5%
9206
7.5%
8202
7.4%
2201
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.498
18.2%
4284
10.4%
5264
9.6%
6250
9.1%
3249
9.1%
7221
8.1%
1206
7.5%
9206
7.5%
8202
7.4%
2201
7.3%

Indice_de_ventas_comercio_real_IVCM
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct82
Distinct (%)97.6%
Missing530
Missing (%)86.3%
Memory size4.9 KiB
116.203.109
 
2
113.525.936
 
2
11.422.079
 
1
150.150.468
 
1
11.265.004
 
1
Other values (77)
77 

Length

Max length11
Median length11
Mean length10.9047619
Min length9

Characters and Unicode

Total characters916
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)95.2%

Sample

1st row940.249.286
2nd row911.554.678
3rd row1.014.057
4th row953.361.487
5th row998.288.464

Common Values

ValueCountFrequency (%)
116.203.1092
 
0.3%
113.525.9362
 
0.3%
11.422.0791
 
0.2%
150.150.4681
 
0.2%
11.265.0041
 
0.2%
116.076.2471
 
0.2%
108.642.1611
 
0.2%
106.941.4711
 
0.2%
111.068.7921
 
0.2%
111.219.5921
 
0.2%
Other values (72)72
 
11.7%
(Missing)530
86.3%

Length

2021-07-28T19:41:02.802296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
116.203.1092
 
2.4%
113.525.9362
 
2.4%
940.249.2861
 
1.2%
11.422.0791
 
1.2%
150.150.4681
 
1.2%
11.265.0041
 
1.2%
116.076.2471
 
1.2%
108.642.1611
 
1.2%
106.941.4711
 
1.2%
111.068.7921
 
1.2%
Other values (72)72
85.7%

Most occurring characters

ValueCountFrequency (%)
.168
18.3%
1138
15.1%
097
10.6%
986
9.4%
375
8.2%
868
7.4%
466
 
7.2%
266
 
7.2%
756
 
6.1%
551
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number748
81.7%
Other Punctuation168
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1138
18.4%
097
13.0%
986
11.5%
375
10.0%
868
9.1%
466
8.8%
266
8.8%
756
7.5%
551
 
6.8%
645
 
6.0%
Other Punctuation
ValueCountFrequency (%)
.168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common916
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.168
18.3%
1138
15.1%
097
10.6%
986
9.4%
375
8.2%
868
7.4%
466
 
7.2%
266
 
7.2%
756
 
6.1%
551
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.168
18.3%
1138
15.1%
097
10.6%
986
9.4%
375
8.2%
868
7.4%
466
 
7.2%
266
 
7.2%
756
 
6.1%
551
 
5.6%

Indice_de_ventas_comercio_real_no_durables_IVCM
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct82
Distinct (%)97.6%
Missing530
Missing (%)86.3%
Memory size4.9 KiB
107.385.297
 
2
103.995.337
 
2
106.510.546
 
1
142.725.968
 
1
106.018.941
 
1
Other values (77)
77 

Length

Max length11
Median length11
Mean length10.91666667
Min length7

Characters and Unicode

Total characters917
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)95.2%

Sample

1st row943.909.236
2nd row928.126.259
3rd row101.583.412
4th row963.546.529
5th row998.625.376

Common Values

ValueCountFrequency (%)
107.385.2972
 
0.3%
103.995.3372
 
0.3%
106.510.5461
 
0.2%
142.725.9681
 
0.2%
106.018.9411
 
0.2%
107.351.3761
 
0.2%
102.861.0041
 
0.2%
101.140.7221
 
0.2%
105.120.5121
 
0.2%
104.502.2851
 
0.2%
Other values (72)72
 
11.7%
(Missing)530
86.3%

Length

2021-07-28T19:41:03.017264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
107.385.2972
 
2.4%
103.995.3372
 
2.4%
943.909.2361
 
1.2%
106.510.5461
 
1.2%
142.725.9681
 
1.2%
106.018.9411
 
1.2%
107.351.3761
 
1.2%
102.861.0041
 
1.2%
101.140.7221
 
1.2%
105.120.5121
 
1.2%
Other values (72)72
85.7%

Most occurring characters

ValueCountFrequency (%)
.167
18.2%
1137
14.9%
0103
11.2%
979
8.6%
870
7.6%
369
7.5%
763
 
6.9%
661
 
6.7%
260
 
6.5%
557
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number750
81.8%
Other Punctuation167
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1137
18.3%
0103
13.7%
979
10.5%
870
9.3%
369
9.2%
763
8.4%
661
8.1%
260
8.0%
557
7.6%
451
 
6.8%
Other Punctuation
ValueCountFrequency (%)
.167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common917
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.167
18.2%
1137
14.9%
0103
11.2%
979
8.6%
870
7.6%
369
7.5%
763
 
6.9%
661
 
6.7%
260
 
6.5%
557
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.167
18.2%
1137
14.9%
0103
11.2%
979
8.6%
870
7.6%
369
7.5%
763
 
6.9%
661
 
6.7%
260
 
6.5%
557
 
6.2%

Indice_de_ventas_comercio_real_durables_IVCM
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct83
Distinct (%)97.6%
Missing529
Missing (%)86.2%
Memory size4.9 KiB
15.342.321
 
2
15.375.472
 
2
146.765.828
 
1
181.489.379
 
1
140.639.995
 
1
Other values (78)
78 

Length

Max length11
Median length11
Mean length10.84705882
Min length10

Characters and Unicode

Total characters922
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)95.3%

Sample

1st row924.800.591
2nd row841.605.817
3rd row100.655.575
4th row910.370.289
5th row996.866.358

Common Values

ValueCountFrequency (%)
15.342.3212
 
0.3%
15.375.4722
 
0.3%
146.765.8281
 
0.2%
181.489.3791
 
0.2%
140.639.9951
 
0.2%
152.904.0451
 
0.2%
133.044.4981
 
0.2%
131.426.5081
 
0.2%
13.617.6561
 
0.2%
139.573.4321
 
0.2%
Other values (73)73
 
11.9%
(Missing)529
86.2%

Length

2021-07-28T19:41:03.241087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15.342.3212
 
2.4%
15.375.4722
 
2.4%
924.800.5911
 
1.2%
13.617.6561
 
1.2%
13.051.3841
 
1.2%
181.489.3791
 
1.2%
140.639.9951
 
1.2%
152.904.0451
 
1.2%
133.044.4981
 
1.2%
131.426.5081
 
1.2%
Other values (73)73
85.9%

Most occurring characters

ValueCountFrequency (%)
.170
18.4%
1129
14.0%
583
9.0%
373
7.9%
472
7.8%
870
7.6%
669
7.5%
769
7.5%
965
 
7.0%
265
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number752
81.6%
Other Punctuation170
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1129
17.2%
583
11.0%
373
9.7%
472
9.6%
870
9.3%
669
9.2%
769
9.2%
965
8.6%
265
8.6%
057
7.6%
Other Punctuation
ValueCountFrequency (%)
.170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.170
18.4%
1129
14.0%
583
9.0%
373
7.9%
472
7.8%
870
7.6%
669
7.5%
769
7.5%
965
 
7.0%
265
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.170
18.4%
1129
14.0%
583
9.0%
373
7.9%
472
7.8%
870
7.6%
669
7.5%
769
7.5%
965
 
7.0%
265
 
7.0%

Ventas_autos_nuevos
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct143
Distinct (%)98.6%
Missing469
Missing (%)76.4%
Infinite0
Infinite (%)0.0%
Mean26694.08276
Minimum4658
Maximum39263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2021-07-28T19:41:03.341819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4658
5-th percentile11480
Q123701
median27912
Q331800
95-th percentile36483
Maximum39263
Range34605
Interquartile range (IQR)8099

Descriptive statistics

Standard deviation7170.231956
Coefficient of variation (CV)0.268607542
Kurtosis0.7946291008
Mean26694.08276
Median Absolute Deviation (MAD)4156
Skewness-0.8758321427
Sum3870642
Variance51412226.3
MonotonicityNot monotonic
2021-07-28T19:41:03.461529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330592
 
0.3%
387292
 
0.3%
285771
 
0.2%
197251
 
0.2%
67061
 
0.2%
67031
 
0.2%
119911
 
0.2%
46581
 
0.2%
115841
 
0.2%
295871
 
0.2%
Other values (133)133
 
21.7%
(Missing)469
76.4%
ValueCountFrequency (%)
46581
0.2%
67031
0.2%
67061
0.2%
86811
0.2%
89061
0.2%
89711
0.2%
100581
0.2%
114541
0.2%
115841
0.2%
119911
0.2%
ValueCountFrequency (%)
392631
0.2%
387292
0.3%
380251
0.2%
379251
0.2%
371321
0.2%
365951
0.2%
365431
0.2%
362431
0.2%
354701
0.2%
354611
0.2%

Interactions

2021-07-28T19:40:29.776733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:29.910774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.011504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.107453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.210177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.309911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.413634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.505388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.596145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.701225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.793977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.873764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:30.958434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.037255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.131207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.219504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.303280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.473159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.561430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.651289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.741048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.831806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:31.919355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.010224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.095541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.195422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.289705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.380705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.480439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.577180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.671926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.763682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.853442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:32.945196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.026013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.111750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.205500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.292268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.376044image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.465346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.553134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.655012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.747195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.830817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:33.926281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:34.126352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:34.228734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:34.323483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:34.411245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:34.508268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:34.610556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:34.710290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:34.815380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:34.913260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.008308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.106047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.201730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.293376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.378150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.466497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.555794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.643559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.729330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-28T19:40:35.820087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-28T19:41:03.564254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-28T19:41:03.736305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-28T19:41:03.910386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-28T19:41:04.186650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-28T19:41:05.905095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-28T19:40:36.341789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-07-28T19:40:39.263122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-07-28T19:40:43.113260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PeriodoImacec_empalmadoImacec_produccion_de_bienesImacec_mineroImacec_industriaImacec_resto_de_bienesImacec_comercioImacec_serviciosImacec_a_costo_de_factoresImacec_no_mineroPIB_Agropecuario_silvicolaPIB_PescaPIB_MineriaPIB_Mineria_del_cobrePIB_Otras_actividades_minerasPIB_Industria_ManufactureraPIB_AlimentosPIB_Bebidas_y_tabacoPIB_TextilPIB_Maderas_y_mueblesPIB_CelulosaPIB_Refinacion_de_petroleoPIB_QuimicaPIB_Minerales_no_metalicos_y_metalica_basicaPIB_Productos_metalicosPIB_ElectricidadPIB_ConstruccionPIB_ComercioPIB_Restaurantes_y_hotelesPIB_TransportePIB_ComunicacionesPIB_Servicios_financierosPIB_Servicios_empresarialesPIB_Servicios_de_viviendaPIB_Servicios_personalesPIB_Administracion_publicaPIB_a_costo_de_factoresImpuesto_al_valor_agregadoDerechos_de_ImportacionPIBPrecio_de_la_gasolina_en_EEUU_dolaresm3Precio_de_la_onza_troy_de_oro_dolaresozPrecio_de_la_onza_troy_de_plata_dolaresozPrecio_del_cobre_refinado_BML_dolareslibraPrecio_del_diesel_centavos_de_dolargalonPrecio_del_gas_natural_dolaresmillon_de_unidades_termicas_britanicasPrecio_del_petroleo_Brent_dolaresbarrilPrecio_del_kerosene_dolaresm3Precio_del_petroleo_WTI_dolaresbarrilPrecio_del_propano_centavos_de_dolargalon_DTNTipo_de_cambio_del_dolar_observado_diarioOcupadosOcupacion_en_Agricultura_INEOcupacion_en_Explotacion_de_minas_y_canteras_INEOcupacion_en_Industrias_manufactureras_INEOcupacion_en_Suministro_de_electricidad_INEOcupacion_en_Actividades_de_servicios_administrativos_y_de_apoyo_INEOcupacion_en_Actividades_profesionales_INEOcupacion_en_Actividades_inmobiliarias_INEOcupacion_en_Actividades_financieras_y_de_seguros_INEOcupacion_en_Informacion_y_comunicaciones_INEOcupacion_en_Transporte_y_almacenamiento_INEOcupacion_en_Actividades_de_alojamiento_y_de_servicio_de_comidas_INEOcupacion_en_Construccion_INEOcupacion_en_Comercio_INEOcupacion_en_Suministro_de_agua_evacuacion_de_aguas_residuales_INEOcupacion_en_Administracion_publica_y_defensa_INEOcupacion_en_Enseanza_INEOcupacion_en_Actividades_de_atencion_de_la_salud_humana_y_de_asistencia_social_INEOcupacion_en_Actividades_artisticas_INEOcupacion_en_Otras_actividades_de_servicios_INEOcupacion_en_Actividades_de_los_hogares_como_empleadores_INEOcupacion_en_Actividades_de_organizaciones_y_organos_extraterritoriales_INENo_sabe__No_responde_Miles_de_personasTipo_de_cambio_nominal_multilateral___TCMIndice_de_tipo_de_cambio_real___TCR_promedio_1986_100Indice_de_produccion_industrialIndice_de_produccion_industrial__mineriaIndice_de_produccion_industrial_electricidad__gas_y_aguaIndice_de_produccion_industrial__manufactureraGeneracion_de_energia_electrica_CDEC_GWhIndice_de_ventas_comercio_real_IVCMIndice_de_ventas_comercio_real_no_durables_IVCMIndice_de_ventas_comercio_real_durables_IVCMVentas_autos_nuevos
02013-03-01 00:00:00 UTC102.796.21810.546.824978.554.365102.297.537114.994.919110.729.395100.064.328103.163.651103.405.852579.846.819570.624.122123.491.343110.356.254131.350.889130.648.285367.818.436162.692.807330.305.467643.366.109974.948.182103.469.519184.460.073807.709.837212.409.058301.314.934710.242.818115.489.064202.838.669546.694.889337.915.2635.691.83711.393.886793.471.519140.103.913512.994.015108.482.797896.688.023659.936.009118.109.6137.638.0221593.60288.157347.586.864301.113.8117109.2476.520.24692.9189.171472.484799.068.585798.092.771258.428.756901.504.814360.108.49418.951.548229.653.619547.033.12419.153.327155.736.824498.854.717341.857.582682.423.108151.916.628349.444.258445.435.266587.837.412362.517.85682.567.788201.875.908416.124.956190.085.519NaN93.621865.903.468102.761.70598.915.705NaN108.387.837580.480.672NaNNaNNaN28577.0
12013-04-01 00:00:00 UTC101.664.842999.272.757927.333.293104.485.589102.199.311106.098.291102.600.107101.937.319102.766.884374.895.706606.504.66511.702.73610.358.806134.393.001133.442.73538.211.344188.189.562380.345.362684.106.836945.996.965824.530.386172.638.981831.959.074224.791.502280.438.294749.055.837110.658.893209.310.713544.306.818348.801.015565.071.466121.229.49180.455.108144.685.333511.803.995107.193.235901.755.56359.890.634116.809.69771.088.2941485.03252.768326.742.266287.604.1674102.8771.791.06692.0293.857472.137.273801.906.655748.761.34625.564.008872.935.00835.161.487189.184.206245.403.126517.938.307200.181.753163.543.99650.361.22231.736.525714.330.143155.050.122357.165.171437.306.264609.994.33136.143.856897.101.326202.053.864433.420.434101.278.125NaN934.109.091867.806.065968.087.179914.276.663NaN104.545.429544.815.032NaNNaNNaN32206.0
22013-05-01 00:00:00 UTC101.642.954993.959.92296.133.164105.445.361968.789.055100.462.117104.083.216101.953.299102.322.796311.356.127545.573.168121.317.874107.784.409135.334.655134.668.49837.345.015184.115.565342.850.811686.925.227104.685.345101.629.225173.785.919778.426.782228.198.491274.899.81774.795.83410.478.045206.481.563536.681.481355.711.814571.394.036125.792.137805.307.434147.859.706512.469.359107.210.039913.850.432436.004.645116.784.5487.166.4251416.59230.894327.913284.564.0423103.0371.318.14894.7693.351479.582.857801.826.939689.928.4122.630.406885.031.604370.478.187183.186.272259.778.478523.538.906204.803.422164.546.013502.470.76130.683.457703.343.89615.611.683387.134.632429.622.30163.826.243364.909.451928.991.289200.806.454438.571.15495.097.066NaN944.752.381878.030.947978.471.874966.913.278NaN99.468.80156.891.916NaNNaNNaN31589.0
32013-06-01 00:00:00 UTC998.310.201968.367.884102.400.933999.212.10689.190.493939.504.414103.857.161100.222.381995.139.235246.592.082418.668.119129.227.656115.404.932138.227.237127.613.384323.857.81517.224.193290.637.238667.221.833102.091.894100.279.607187.920.877716.634.523222.292.356270.141.266719.957.056979.888.723208.021.235526.589.163353.452.988573.271.479125.565.876811.366.828146.903.407514.736.611105.389.875883.855.814474.261.003114.702.69470.787.1061342.47211.186317.701.624285.993.8246103.1172.065.83495.7986.188502.886800.424.589643.035.387271.231.704893.552.645393.545.812174.068.814256.673.756541.516.67219.556.226167.439.052514.471.17730.862.596698.814.201156.308.276398.815.137429.793.264648.435.359364.323.921922.830.791201.437.383447.361.17466.623.637NaN985.085913.180.348966.647.135981.820.974NaN94.969.67856.594.867NaNNaNNaN28457.0
42013-07-01 00:00:00 UTC964.696.194961.051.418980.029.022100.882.112901.785.88692.776.599969.309.272962.069.134962.804.305201.156.433565.897.618123.677.44110.915.864145.188.012128.840.589327.206.582163.572.934277.405.414692.167.09710.693.519102.108.993179.849.331763.622.68823.541.334293.379.905741.595.595967.645.727212.827.237536.446.432353.028.667574.971.364124.038.548813.086.052108.506.835515.375.539101.167.369912.662.417546.557.71211.084.05575.233.5921288.01197.571312.663.522298.093.6242107.727.488.749104.7092.179504.962.273798.681.519625.955.595268.516.51990.967.219422.440.362163.224.557249.403.461685.736.504190.390.057158.353.388509.933.563326.787.184685.679.3311.561.431397.905.417421.899.254675.429.113367.557.412961.558.663187.860.463436.743.687121.432.645NaN982.236.364909.263.447100.100.749990.198.382NaN102.128.977586.329.899NaNNaNNaN31736.0
52013-08-01 00:00:00 UTC993.089.872959.559.679102.692.68299.581.119867.956.695940.499.299102.531.671992.062.162988.914.796164.442.453383.205.833129.595.837115.632.385139.634.515127.179.039319.607.283164.911.305279.789.343678.351.474105.715.824968.810.918193.952.851741.481.188220.759.836291.412.898750.051.178980.926.373211.268.013540.388.112352.172.135578.992.271119.632.005814.445.208142.856.82951.707.529104.321.316919.935.114582.227.245114.102.89575.516.2861353.68221.349325.785.176304.163.4240110.9777.006.374106.55106.176512.588.571800.857.678630.589.029262.440.542910.927.777392.675.058163.608.943247.125.435706.500.119196.804.463153.633.456523.609.393314.039.287691.034.832156.293.431410.347.72941.632.397684.208.267371.144.123108.706.852193.833.718425.277.775138.231.188NaN997.733.333924.327.171100.982.205100.650.063NaN102.477.451581.916.587NaNNaNNaN27984.0
62013-09-01 00:00:00 UTC950.851.786913.878.449103.290.693903.876.015818.004.363877.378.05398.778.4619.489.134940.727.157160.477.31438.894.818130.350.514116.426.531139.239.832115.437.62926.903.76116.188.931263.089.461587.157.951974.840.776101.446.912175.552.261643.792.63519.956.212295.928.009677.319.65891.509.188214.948.942522.534.625350.536.876575.223.161119.033.996816.734.407124.279.9895.196.854997.839.638889.834.518567.562.681109.249.87268.750.1241350.79226.009324.840.334301.513.6154111.6276.678.766106.25110.738504.5780.108.426622.841.156258.601.644932.878.606349.738.637160.461.593254.473.527753.165.571192.214.998160.669.968513.474.148306.144.906680.073.337157.232.856453.850.945416.148.722687.908.172368.867.999108.580.053194.131.279422.507.539286.087.435NaN982.022.2229.091.755953.781.211999.413.903NaN90.189.758540.682.811NaNNaNNaN32220.0
72013-10-01 00:00:00 UTC102.727.926101.742.025106.829.225102.778.3789.634.026978.012.485104.484.802102.785.719102.221.875221.537.727729.880.253134.816.06111.914.387156.721.905131.262.387332.156.661163.386.082351.670.674697.796.583100.745.164867.785.395206.593.736856.227.102232.394.253309.781.655776.742.641102.005.205215.304.331564.507.613376.139.453573.619.706121.332.368812.608.461147.113.476520.013.321108.085.379937.537.895570.389.466118.031.14765.685.4041315.89220.061326.079.107293.603.6751109.4876.015.624100.50113.472500.806.364802.959.737617.614.843259.591.002921.094.885331.917.239165.288.709256.954.505734.249.956191.689.106171.779.053516.649.307302.878.966673.738.075157.098.982433.120.175423.307.678699.386.802373.953.321108.119.517216.000.416406.585.395404.723.309NaN983.959.091909.270.935104.054.621104.866.643NaN104.775.367577.575.914NaNNaNNaN31380.0
82013-11-01 00:00:00 UTC105.750.158103.950.101108.805.134100.421.86610.281.939990.638.058108.478.482105.833.94110.537.321258.440.296671.917.472137.309.613121.969.788153.398.247128.252.791324.699.027162.586.968316.389.079676.444.763102.836.976819.567.375202.896.93276.997.862231.270.026303.561.665844.735.502103.322.033228.180.032560.489.808396.580.189576.603.175134.694.254852.677.224148.348.667521.343.578111.290.768969.553.876517.290.147121.503.59764.760.7041275.18206.932320.529.801285.673.6196108.0874.443.63493.89118.086519.2580.671.274628.110.671263.251.118909.999.631332.188.151183.561.196257.621.563693.810.323183.212.117178.184.407514.296.669316.195.979682.130.121157.609.468416.627.202424.697.976697.236.137372.592.794112.433.258226.491.793392.439.37843.153.476NaN101.162929.594.435104.115.839106.068.198NaN103.490.814560.293.741NaNNaNNaN34358.0
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Last rows

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